https://purl.org/ontodt/dmtypes https://purl.org/ontodt https://purl.org/ontodm/external Ontology of Core Data Mining Entities true Saso Dzeroski 0.2 Pance Panov English 2.0 Pance Panov Ana Kostovska Data Mining and Knowledge Discovery OntoDM-core OntoDM-core English Pance Panov Larisa Soldatova http://kt.ijs.si/panovp/OntoDM/OntoDM.owl OWL-DL http://kt.ijs.si/panovp/OntoDM#OntoDM_000002 constraints_set http://kt.ijs.si/panovp/OntoDM#OntoDM_000005 pre_processing_algorithm_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000018 numeric_datatype http://kt.ijs.si/panovp/OntoDM#OntoDM_000024 data_object http://kt.ijs.si/panovp/OntoDM#OntoDM_000025 In the case of learning probabilistic models, we need to find a mapping of the form m::Td-->(Tc-->R0+). In the more general formulation of the problem, the training examples can have probability distributions over the c values insdead having the c values by itself. http://kt.ijs.si/panovp/OntoDM#OntoDM_000025 probabilistic_predictive_modeling_task http://kt.ijs.si/panovp/OntoDM#OntoDM_000032 prototype_calculation_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000035 search_function_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000040 distance_function_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000046 distance_calculation_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000056 evaluation_method_parameter http://kt.ijs.si/panovp/OntoDM#OntoDM_000062 refinement_relation_generation http://kt.ijs.si/panovp/OntoDM#OntoDM_000064 primitive_feature_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000073 Probabilistic predictive model P for types Td and Tc is a function that takes an object of type Td and return a probabiliy distribution over type Tc, i.e. has the signature p::Tc-->(Td-->R0+) http://kt.ijs.si/panovp/OntoDM#OntoDM_000073 probabilistic_predictive_model http://kt.ijs.si/panovp/OntoDM#OntoDM_000079 refinement_operator_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000086 feature_generation_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000115 data_values http://kt.ijs.si/panovp/OntoDM#OntoDM_000117 basic_data_mining_component_role http://kt.ijs.si/panovp/OntoDM#OntoDM_000123 post_processing_task http://kt.ijs.si/panovp/OntoDM#OntoDM_000129 generalization_role http://kt.ijs.si/panovp/OntoDM#OntoDM_000138 base_learners_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000139 post_processing_algorithm_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000146 have to specify the output here! http://kt.ijs.si/panovp/OntoDM#OntoDM_000146 kernel_calculation_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000148 information_content_entity_agregate http://kt.ijs.si/panovp/OntoDM#OntoDM_000152 pre_processing_algorithm_implementation http://kt.ijs.si/panovp/OntoDM#OntoDM_000153 target_feature_set_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000157 kernel_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000164 post_processing_algorithm_implementation http://kt.ijs.si/panovp/OntoDM#OntoDM_000171 example_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000171 data_item_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000174 refinement_operator_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000176 assignment_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000180 fold_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000187 feature_generation_role http://kt.ijs.si/panovp/OntoDM#OntoDM_000188 structured_feature_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000190 Describes set of parameters http://kt.ijs.si/panovp/OntoDM#OntoDM_000190 parameters_set_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000194 datatype_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000203 dataset_role http://kt.ijs.si/panovp/OntoDM#OntoDM_000208 parameter_role http://kt.ijs.si/panovp/OntoDM#OntoDM_000218 bag_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000221 function_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000222 data_role http://kt.ijs.si/panovp/OntoDM#OntoDM_000236 scenario_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000245 projection_objective http://kt.ijs.si/panovp/OntoDM#OntoDM_000257 assesement specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000274 pre_processing_task http://kt.ijs.si/panovp/OntoDM#OntoDM_000277 stuctured_datatype_construction http://kt.ijs.si/panovp/OntoDM#OntoDM_000278 An interpreter takes as input the data part of a pattern and an example and returns the result of applying the function part of the pattern to the example. http://kt.ijs.si/panovp/OntoDM#OntoDM_000278 interpretation_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000279 descriptive_feature_set_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000284 dataset_projection_process http://kt.ijs.si/panovp/OntoDM#OntoDM_000289 data_use_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000295 query_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000296 prototype_function_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000301 kernel_function_specification http://kt.ijs.si/panovp/OntoDM#OntoDM_000304 distance_representation http://kt.ijs.si/panovp/OntoDM#OntoDM_000307 unlabeled_example http://kt.ijs.si/panovp/OntoDM#OntoDM_000307 unlabeled_data_item http://kt.ijs.si/panovp/OntoDM#OntoDM_000310 dataset_projection http://kt.ijs.si/panovp/OntoDM#OntoDM_251805 OntoDM_251805 http://kt.ijs.si/panovp/OntoDM#OntoDM_251805 matematical function http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_269620 OntoDM_269620s http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_269620 OntoDM_269620 http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_269620 precentage train-test split evaluation algorithm http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_349672 OntoDM_349672s http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_349672 OntoDM_349672 http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_349672 separate test set evaluation algorithm http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_361694 OntoDM_361694s http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_361694 OntoDM_361694 http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_361694 cross validation evaluation algorithm http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_562975 OntoDM_562975s http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_562975 OntoDM_562975 http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_562975 train set evaluation algorithm http://ontodm.com/OntoDT#OntoDT_018988 OntoDT_018988s http://ontodm.com/OntoDT#OntoDT_018988 OntoDT_018988 http://ontodm.com/OntoDT#OntoDT_150012 OntoDT_150012s http://ontodm.com/OntoDT#OntoDT_150012 OntoDT_150012 http://ontodm.com/OntoDT#OntoDT_245924 OntoDT_245924s http://ontodm.com/OntoDT#OntoDT_245924 OntoDT_245924 http://ontodm.com/OntoDT#OntoDT_360708 OntoDT_360708s http://ontodm.com/OntoDT#OntoDT_360708 OntoDT_360708 http://ontodm.com/OntoDT#OntoDT_377271 OntoDT_377271s http://ontodm.com/OntoDT#OntoDT_377271 OntoDT_377271 http://ontodm.com/OntoDT#OntoDT_378476 OntoDT_378476s http://ontodm.com/OntoDT#OntoDT_378476 OntoDT_378476 http://ontodm.com/OntoDT#OntoDT_379444 OntoDT_379444s http://ontodm.com/OntoDT#OntoDT_379444 OntoDT_379444 http://ontodm.com/OntoDT#OntoDT_471356 OntoDT_471356s http://ontodm.com/OntoDT#OntoDT_471356 OntoDT_471356 http://ontodm.com/OntoDT#OntoDT_487147 OntoDT_487147s http://ontodm.com/OntoDT#OntoDT_487147 OntoDT_487147 http://ontodm.com/OntoDT#OntoDT_521859 OntoDT_521859s http://ontodm.com/OntoDT#OntoDT_521859 OntoDT_521859 http://ontodm.com/OntoDT#OntoDT_591397 OntoDT_591397s http://ontodm.com/OntoDT#OntoDT_591397 OntoDT_591397 http://ontodm.com/OntoDT#OntoDT_595741 OntoDT_595741s http://ontodm.com/OntoDT#OntoDT_595741 OntoDT_595741 http://ontodm.com/OntoDT#OntoDT_596819 OntoDT_596819s http://ontodm.com/OntoDT#OntoDT_596819 OntoDT_596819 http://ontodm.com/OntoDT#OntoDT_608148 OntoDT_608148s http://ontodm.com/OntoDT#OntoDT_608148 OntoDT_608148 http://ontodm.com/OntoDT#OntoDT_645869 OntoDT_645869s http://ontodm.com/OntoDT#OntoDT_645869 OntoDT_645869 http://ontodm.com/OntoDT#OntoDT_648082 OntoDT_648082s http://ontodm.com/OntoDT#OntoDT_648082 OntoDT_648082 http://ontodm.com/OntoDT#OntoDT_699579 OntoDT_699579s http://ontodm.com/OntoDT#OntoDT_699579 OntoDT_699579 http://ontodm.com/OntoDT#OntoDT_814373 OntoDT_814373s http://ontodm.com/OntoDT#OntoDT_814373 OntoDT_814373 http://ontodm.com/OntoDT#OntoDT_878284 OntoDT_878284s http://ontodm.com/OntoDT#OntoDT_878284 OntoDT_878284 http://ontodm.com/OntoDT#OntoDT_950419 OntoDT_950419s http://ontodm.com/OntoDT#OntoDT_950419 OntoDT_950419 http://ontodm.com/OntoDT#OntoDT_974622 OntoDT_974622s http://ontodm.com/OntoDT#OntoDT_974622 OntoDT_974622 http://ontodm.com/OntoDT#OntoDT_991825 OntoDT_991825 http://purl.obofoundry.org/obo/OBI_0000022 http://purl.obofoundry.org/obo/obi.owl http://purl.obofoundry.org/obo/OBI_0000022 specified_input_role http://purl.obofoundry.org/obo/OBI_0000023 http://purl.obofoundry.org/obo/obi.owl http://purl.obofoundry.org/obo/OBI_0000023 specified_output_role http://purl.obofoundry.org/obo/OBI_0000069 OBI_0000069s http://purl.obofoundry.org/obo/OBI_0000069 OBI_0000069 http://purl.obofoundry.org/obo/OBI_0000069 http://purl.obofoundry.org/obo/obi.owl http://purl.obofoundry.org/obo/OBI_0000260 OBI_0000260s http://purl.obofoundry.org/obo/OBI_0000260 OBI_0000260 http://purl.obofoundry.org/obo/OBI_0000260 http://purl.obofoundry.org/obo/obi.owl http://purl.obofoundry.org/obo/OBI_0000260 plan http://purl.obofoundry.org/obo/OBI_0000283 Annotation property imported from OBI to denote terms that have been imported from other ontologies http://purl.obofoundry.org/obo/OBI_0000283 imported_from http://purl.obofoundry.org/obo/OBI_0000587 OBI_0000587s http://purl.obofoundry.org/obo/OBI_0000587 OBI_0000587 http://purl.obofoundry.org/obo/OBI_0000587 http://purl.obofoundry.org/obo/obi.owl http://purl.obofoundry.org/obo/OBI_0000587 protocol_paricipant_role http://purl.obolibrary.org/obo/BFO_0000017 RealizableEntities http://purl.obolibrary.org/obo/BFO_0000017 RealizableEntity http://purl.obolibrary.org/obo/BFO_0000019 Qualities http://purl.obolibrary.org/obo/BFO_0000019 Quality http://purl.obolibrary.org/obo/BFO_0000023 Roles http://purl.obolibrary.org/obo/BFO_0000023 Role http://purl.obolibrary.org/obo/IAO_0000005 IAO_0000005s http://purl.obolibrary.org/obo/IAO_0000005 IAO_0000005 http://purl.obolibrary.org/obo/IAO_0000005 http://purl.obofoundry.org/obo/iao.owl http://purl.obolibrary.org/obo/IAO_0000009 IAO_0000009s http://purl.obolibrary.org/obo/IAO_0000009 IAO_0000009 http://purl.obolibrary.org/obo/IAO_0000010 IAO_0000010s http://purl.obolibrary.org/obo/IAO_0000010 IAO_0000010 http://purl.obolibrary.org/obo/IAO_0000025 IAO_0000025s http://purl.obolibrary.org/obo/IAO_0000025 IAO_0000025 http://purl.obolibrary.org/obo/IAO_0000027 IAO_0000027s http://purl.obolibrary.org/obo/IAO_0000027 IAO_0000027 http://purl.obolibrary.org/obo/IAO_0000028 IAO_0000028s http://purl.obolibrary.org/obo/IAO_0000028 IAO_0000028 http://purl.obolibrary.org/obo/IAO_0000030 IAO_0000030s http://purl.obolibrary.org/obo/IAO_0000030 IAO_0000030 http://purl.obolibrary.org/obo/IAO_0000030 http://purl.obofoundry.org/obo/iao.owl http://purl.obolibrary.org/obo/IAO_0000033 IAO_0000033s http://purl.obolibrary.org/obo/IAO_0000033 IAO_0000033 http://purl.obolibrary.org/obo/IAO_0000033 http://purl.obofoundry.org/obo/iao.owl http://purl.obolibrary.org/obo/IAO_0000033 directive information entity http://purl.obolibrary.org/obo/IAO_0000064 IAO_0000064s http://purl.obolibrary.org/obo/IAO_0000064 IAO_0000064 http://purl.obolibrary.org/obo/IAO_0000065 IAO_0000065s http://purl.obolibrary.org/obo/IAO_0000065 IAO_0000065 http://purl.obolibrary.org/obo/IAO_0000088 IAO_0000088s http://purl.obolibrary.org/obo/IAO_0000088 IAO_0000088 http://purl.obolibrary.org/obo/IAO_0000096 IAO_0000096s http://purl.obolibrary.org/obo/IAO_0000096 IAO_0000096 http://purl.obolibrary.org/obo/IAO_0000098 IAO_0000098s http://purl.obolibrary.org/obo/IAO_0000098 IAO_0000098 http://purl.obolibrary.org/obo/IAO_0000100 IAO_0000100s http://purl.obolibrary.org/obo/IAO_0000100 IAO_0000100 http://purl.obolibrary.org/obo/IAO_0000104 IAO_0000104s http://purl.obolibrary.org/obo/IAO_0000104 IAO_0000104 http://purl.obolibrary.org/obo/IAO_0000124 IAO_0000124s http://purl.obolibrary.org/obo/IAO_0000124 IAO_0000124 http://purl.obolibrary.org/obo/IAO_0000129 IAO_0000129s http://purl.obolibrary.org/obo/IAO_0000129 IAO_0000129 http://purl.obolibrary.org/obo/IAO_0000136 IAO_0000136 http://purl.obolibrary.org/obo/IAO_0000136 IAO_0000136s http://purl.obolibrary.org/obo/IAO_0000136 IAO_0000136ed http://purl.obolibrary.org/obo/IAO_0000136 http://purl.obofoundry.org/obo/iao.owl http://purl.obolibrary.org/obo/IAO_0000136 is_about http://purl.obolibrary.org/obo/IAO_0000300 IAO_0000300s http://purl.obolibrary.org/obo/IAO_0000300 IAO_0000300 http://purl.obolibrary.org/obo/IAO_0000302 IAO_0000302s http://purl.obolibrary.org/obo/IAO_0000302 IAO_0000302 http://purl.obolibrary.org/obo/IAO_0000310 IAO_0000310s http://purl.obolibrary.org/obo/IAO_0000310 IAO_0000310 http://purl.obolibrary.org/obo/IAO_0000310 document http://purl.obolibrary.org/obo/IAO_0000311 IAO_0000311s http://purl.obolibrary.org/obo/IAO_0000311 IAO_0000311 http://purl.obolibrary.org/obo/IAO_0000314 IAO_0000314s http://purl.obolibrary.org/obo/IAO_0000314 IAO_0000314 http://purl.obolibrary.org/obo/IAO_0000314 document part http://purl.obolibrary.org/obo/IAO_0000418 IAO_0000418 http://purl.obolibrary.org/obo/IAO_0000418 IAO_0000418s http://purl.obolibrary.org/obo/IAO_0000418 IAO_0000418ed http://purl.obolibrary.org/obo/IAO_0000442 OntoDM_247286s http://purl.obolibrary.org/obo/IAO_0000442 OntoDM_247286 http://purl.obolibrary.org/obo/IAO_0000442 agent role http://purl.obolibrary.org/obo/IAO_0000572 IAO_0000572s http://purl.obolibrary.org/obo/IAO_0000572 IAO_0000572 http://purl.obolibrary.org/obo/IAO_0000572 a planned process of capturing information in an enduring form with the intent to communicate this information http://purl.obolibrary.org/obo/IAO_0000572 documenting http://purl.obolibrary.org/obo/OBI_0000011 OBI_0000011s http://purl.obolibrary.org/obo/OBI_0000011 OBI_0000011 http://purl.obolibrary.org/obo/OBI_0000047 OBI_0000047s http://purl.obolibrary.org/obo/OBI_0000047 OBI_0000047 http://purl.obolibrary.org/obo/OBI_0000047 Is a material entity that is created or changed during material processing. http://purl.obolibrary.org/obo/OBI_0000047 OBI http://purl.obolibrary.org/obo/OBI_0000047 OBI consortium http://purl.obolibrary.org/obo/OBI_0000047 processed material http://purl.obolibrary.org/obo/OBI_0000227 OBI_0000227s http://purl.obolibrary.org/obo/OBI_0000227 OBI_0000227 http://purl.obolibrary.org/obo/OBI_0000245 OBI_0000245s http://purl.obolibrary.org/obo/OBI_0000245 OBI_0000245 http://purl.obolibrary.org/obo/OBI_0000272 OBI_0000272s http://purl.obolibrary.org/obo/OBI_0000272 OBI_0000272 http://purl.obolibrary.org/obo/OBI_0000272 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000272 protocol http://purl.obolibrary.org/obo/OBI_0000293 OBI_0000293 http://purl.obolibrary.org/obo/OBI_0000293 OBI_0000293s http://purl.obolibrary.org/obo/OBI_0000293 OBI_0000293ed http://purl.obolibrary.org/obo/OBI_0000293 has_specified_input http://purl.obolibrary.org/obo/OBI_0000294 OBI_0000294 http://purl.obolibrary.org/obo/OBI_0000294 OBI_0000294s http://purl.obolibrary.org/obo/OBI_0000294 OBI_0000294ed http://purl.obolibrary.org/obo/OBI_0000294 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000294 Is a relationship between a generically dependent continuant and a specifically dependent continuant. A generically dependent continuant may inhere in more than one entity. It does so by virtue of the fact that there is, for each entity that it inheres, a specifically dependent *concretization* of the generically dependent continuant that is specifically dependent. For instance, consider a story, which is an information artifact that inheres in some number of books. Each book bears some quality that carries the story. The relation between this quality and the generically dependent continuant is that the former is the concretization of the latter. http://purl.obolibrary.org/obo/OBI_0000294 is_concretization_of http://purl.obolibrary.org/obo/OBI_0000295 OBI_0000295 http://purl.obolibrary.org/obo/OBI_0000295 OBI_0000295s http://purl.obolibrary.org/obo/OBI_0000295 OBI_0000295ed http://purl.obolibrary.org/obo/OBI_0000295 is_specified_input_of http://purl.obolibrary.org/obo/OBI_0000297 OBI_0000297 http://purl.obolibrary.org/obo/OBI_0000297 OBI_0000297s http://purl.obolibrary.org/obo/OBI_0000297 OBI_0000297ed http://purl.obolibrary.org/obo/OBI_0000297 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000297 Is a relationship between a specifically dependent continuant and a generically dependent continuant. A generically dependent continuant may inhere in more than one entity. It does so by virtue of the fact that there is, for each entity that it inheres, a specifically dependent *concretization* of the generically dependent continuant that is specifically dependent. For instance, consider a story, which is an information artifact that inheres in some number of books. Each book bears some quality that carries the story. The relation between this quality and the generically dependent continuant is that the former is the concretization of the latter. http://purl.obolibrary.org/obo/OBI_0000297 is_concretized_as http://purl.obolibrary.org/obo/OBI_0000298 OBI_0000298 http://purl.obolibrary.org/obo/OBI_0000298 OBI_0000298s http://purl.obolibrary.org/obo/OBI_0000298 OBI_0000298ed http://purl.obolibrary.org/obo/OBI_0000298 has_quality http://purl.obolibrary.org/obo/OBI_0000299 OBI_0000299 http://purl.obolibrary.org/obo/OBI_0000299 OBI_0000299s http://purl.obolibrary.org/obo/OBI_0000299 OBI_0000299ed http://purl.obolibrary.org/obo/OBI_0000299 has_specified_output http://purl.obolibrary.org/obo/OBI_0000300 OBI_0000300 http://purl.obolibrary.org/obo/OBI_0000300 OBI_0000300s http://purl.obolibrary.org/obo/OBI_0000300 OBI_0000300ed http://purl.obolibrary.org/obo/OBI_0000300 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000300 Relation between a realizable and a process. Reciprocal relation of realizes [GOC:cjm] http://purl.obolibrary.org/obo/OBI_0000300 is_realized_by http://purl.obolibrary.org/obo/OBI_0000304 OBI_0000304 http://purl.obolibrary.org/obo/OBI_0000304 OBI_0000304s http://purl.obolibrary.org/obo/OBI_0000304 OBI_0000304ed http://purl.obolibrary.org/obo/OBI_0000304 is_manufactured_by http://purl.obolibrary.org/obo/OBI_0000308 OBI_0000308 http://purl.obolibrary.org/obo/OBI_0000308 OBI_0000308s http://purl.obolibrary.org/obo/OBI_0000308 OBI_0000308ed http://purl.obolibrary.org/obo/OBI_0000308 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000308 Relation between a process and a function, where the unfolding of the process requires the execution of the function. Class level: P realizes F iff: given any p that instantiates P, there exists some f, t such that f instantiates F at t and p *realizes* f. Here, *realizes* is the primitive instance level relation [GOC:cjm] http://purl.obolibrary.org/obo/OBI_0000308 realizes http://purl.obolibrary.org/obo/OBI_0000312 OBI_0000312 http://purl.obolibrary.org/obo/OBI_0000312 OBI_0000312s http://purl.obolibrary.org/obo/OBI_0000312 OBI_0000312ed http://purl.obolibrary.org/obo/OBI_0000312 is_specified_output_of http://purl.obolibrary.org/obo/OBI_0000316 OBI_0000316 http://purl.obolibrary.org/obo/OBI_0000316 OBI_0000316s http://purl.obolibrary.org/obo/OBI_0000316 OBI_0000316ed http://purl.obolibrary.org/obo/OBI_0000316 has_role http://purl.obolibrary.org/obo/OBI_0000338 OBI_0000338s http://purl.obolibrary.org/obo/OBI_0000338 OBI_0000338 http://purl.obolibrary.org/obo/OBI_0000338 the process of evaluating the data gathered in an investigation in the context of literature knowledge with the objective to generate more general conclusions or to identify what additional data is necessary to draw conclusions http://purl.obolibrary.org/obo/OBI_0000338 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000338 interpreting data http://purl.obolibrary.org/obo/OBI_0000339 OBI_0000339s http://purl.obolibrary.org/obo/OBI_0000339 OBI_0000339 http://purl.obolibrary.org/obo/OBI_0000339 planning http://purl.obolibrary.org/obo/OBI_0000417 OBI_0000417 http://purl.obolibrary.org/obo/OBI_0000417 OBI_0000417s http://purl.obolibrary.org/obo/OBI_0000417 OBI_0000417ed http://purl.obolibrary.org/obo/OBI_0000417 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0000417 http://purl.obolibrary.org/obo/OBI_0000417 achieves_planned_objective http://purl.obolibrary.org/obo/OBI_0000658 OBI_0000658s http://purl.obolibrary.org/obo/OBI_0000658 OBI_0000658 http://purl.obolibrary.org/obo/OBI_0000833 OBI_0000833 http://purl.obolibrary.org/obo/OBI_0000833 OBI_0000833s http://purl.obolibrary.org/obo/OBI_0000833 OBI_0000833ed http://purl.obolibrary.org/obo/OBI_0000833 objective_achieved_by http://purl.obolibrary.org/obo/OBI_0000968 OBI_0000968s http://purl.obolibrary.org/obo/OBI_0000968 OBI_0000968 http://purl.obolibrary.org/obo/OBI_0000968 A device is a processed material which is designed to perform some function or functions http://purl.obolibrary.org/obo/OBI_0000968 OBI http://purl.obolibrary.org/obo/OBI_0000968 OBI consortium http://purl.obolibrary.org/obo/OBI_0000968 device http://purl.obolibrary.org/obo/OBI_0302911 OBI_0302911s http://purl.obolibrary.org/obo/OBI_0302911 OBI_0302911 http://purl.obolibrary.org/obo/OBI_0302911 http://purl.obofoundry.org/obo/obi.owl http://purl.obolibrary.org/obo/OBI_0400107 OBI_0400107s http://purl.obolibrary.org/obo/OBI_0400107 OBI_0400107 http://purl.obolibrary.org/obo/OBI_0400107 A computer is an instrument which manipulates (stores, retrieves, and processes) data according to a list of instructions. http://purl.obolibrary.org/obo/OBI_0400107 OBI http://purl.obolibrary.org/obo/OBI_0400107 OBI consortium http://purl.obolibrary.org/obo/OBI_0400107 computer http://purl.obolibrary.org/obo/OBI_0600008 OBI_0600008s http://purl.obolibrary.org/obo/OBI_0600008 OBI_0600008 http://purl.obolibrary.org/obo/OBI_0600008 acquisition http://purl.org/obo/owl/OBO_REL#bearer_of bearer_of http://purl.org/obo/owl/OBO_REL#bearer_of bearer_ofs http://purl.org/obo/owl/OBO_REL#bearer_of bearer_ofed http://purl.org/obo/owl/OBO_REL#bearer_of http://purl.obofoundry.org/obo/obi.owl http://purl.org/obo/owl/OBO_REL#bearer_of bearer_of http://purl.org/obo/owl/OBO_REL#inheres_in inheres_in http://purl.org/obo/owl/OBO_REL#inheres_in inhereses_in http://purl.org/obo/owl/OBO_REL#inheres_in inheresed_in http://purl.org/obo/owl/OBO_REL#inheres_in http://purl.org/obo/owl/OBO_REL http://purl.org/obo/owl/OBO_REL#inheres_in OntoDM_462848 http://purl.org/obo/owl/OBO_REL#inheres_in inheres_in http://purl.org/obo/owl/OBO_REL#quality_of quality_of http://purl.org/obo/owl/OBO_REL#quality_of quality_ofs http://purl.org/obo/owl/OBO_REL#quality_of quality_ofed http://purl.org/obo/owl/OBO_REL#quality_of quality_of http://purl.org/obo/owl/OBO_REL#role_of role_of http://purl.org/obo/owl/OBO_REL#role_of role_ofs http://purl.org/obo/owl/OBO_REL#role_of role_ofed http://purl.org/obo/owl/OBO_REL#role_of http://purl.org/obo/owl/OBO_REL http://purl.org/obo/owl/OBO_REL#role_of role_of http://www.ebi.ac.uk/efo/swo/SWO_0000393 SWO_0000393s http://www.ebi.ac.uk/efo/swo/SWO_0000393 SWO_0000393 http://www.ebi.ac.uk/efo/swo/SWO_0000393 http://www.ebi.ac.uk/efo/swo http://www.ebi.ac.uk/efo/swo/SWO_0000393 Information processing is a process in which input information is analysed or transformed in order to produce information as output. http://www.ebi.ac.uk/efo/swo/SWO_0000393 James Malone http://www.ebi.ac.uk/efo/swo/SWO_0000393 information processing http://www.hozo.jp/owl/EXPOApr19.xml/has_representation has_representation http://www.hozo.jp/owl/EXPOApr19.xml/has_representation has_representations http://www.hozo.jp/owl/EXPOApr19.xml/has_representation has_representationed http://www.hozo.jp/owl/EXPOApr19.xml/has_representation has_representation http://www.ifomis.org/bfo/1.1/span#ProcessualEntity ProcessualEntities http://www.ifomis.org/bfo/1.1/span#ProcessualEntity ProcessualEntity http://www.obofoundry.org/ro/ro.owl#agent_in agent_in http://www.obofoundry.org/ro/ro.owl#agent_in agents_in http://www.obofoundry.org/ro/ro.owl#agent_in agented_in http://www.obofoundry.org/ro/ro.owl#has_agent has_agent http://www.obofoundry.org/ro/ro.owl#has_agent has_agents http://www.obofoundry.org/ro/ro.owl#has_agent has_agented http://www.obofoundry.org/ro/ro.owl#has_part has_part http://www.obofoundry.org/ro/ro.owl#has_part has_parts http://www.obofoundry.org/ro/ro.owl#has_part has_parted http://www.obofoundry.org/ro/ro.owl#has_participant has_participant http://www.obofoundry.org/ro/ro.owl#has_participant has_participants http://www.obofoundry.org/ro/ro.owl#has_participant has_participanted http://www.obofoundry.org/ro/ro.owl#part_of part_of http://www.obofoundry.org/ro/ro.owl#part_of part_ofs http://www.obofoundry.org/ro/ro.owl#part_of part_ofed http://www.obofoundry.org/ro/ro.owl#participates_in participates_in http://www.obofoundry.org/ro/ro.owl#participates_in participateses_in http://www.obofoundry.org/ro/ro.owl#participates_in participatesed_in http://www.obofoundry.org/ro/ro.owl#preceded_by preceded_by http://www.obofoundry.org/ro/ro.owl#preceded_by preceded_bies http://www.obofoundry.org/ro/ro.owl#preceded_by preceded_bied http://www.obofoundry.org/ro/ro.owl#precedes precedes http://www.obofoundry.org/ro/ro.owl#precedes precedeses http://www.obofoundry.org/ro/ro.owl#precedes precedesed OntoDM_000000 OntoDM_000000s OntoDM_000000 OntoDM_000000 OntoDM_000000 prediction_error OntoDM_000001 OntoDM_000001s OntoDM_000001 OntoDM_000001 OntoDM_000001 A predictive modelling ensemble is a realizable entity that is a concretization of a predictive model ensemble specification specification and it is a specified output of a predictive modeling algorithm execution. OntoDM_000001 predictive models ensemble OntoDM_000002 feature-based unlabeled data stream OntoDM_000003 structure-based unlabeled data stream OntoDM_000004 OntoDM_000004s OntoDM_000004 OntoDM_000004 OntoDM_000004 A probability distribution ensemble is a realizable entity that is a concretization of a probability distribution ensemble specification and it is a specified output of a probability distribution estimation algorithm execution. OntoDM_000004 probability distribution ensemble OntoDM_0000045 OntoDM_0000045s OntoDM_0000045 OntoDM_0000045 OntoDM_0000045 SubBag OntoDM_000005 transactional data stream OntoDM_000006 OntoDM_000006s OntoDM_000006 OntoDM_000006 OntoDM_000008 OntoDM_000008s OntoDM_000008 OntoDM_000008 OntoDM_000008 prototype OntoDM_000009 OntoDM_000009s OntoDM_000009 OntoDM_000009 OntoDM_000009 probability distribution representation OntoDM_000011 OntoDM_000011s OntoDM_000011 OntoDM_000011 OntoDM_000011 Set of distance functions is a collection of distance functions. OntoDM_000011 distance functions set OntoDM_000014 OntoDM_000014s OntoDM_000014 OntoDM_000014 OntoDM_000014 Evaluation functions measure the validity of generalizations on a given set of data. OntoDM_000014 evaluation function specification OntoDM_000015 OntoDM_000015s OntoDM_000015 OntoDM_000015 OntoDM_000015 An clustering ensemble is a realizable entity that is a concretization of a clusterings ensemble specification and it is a specified output of a clustering algorithm execution. OntoDM_000015 clustering ensemble OntoDM_000016 OntoDM_000016s OntoDM_000016 OntoDM_000016 OntoDM_000017 OntoDM_000017s OntoDM_000017 OntoDM_000017 OntoDM_000017 test set role OntoDM_000020 OntoDM_000020s OntoDM_000020 OntoDM_000020 OntoDM_000020 A pattern P on type T is a boolean function on objects of type T, i.e has the signature p::T-->boolean. OntoDM_000021 OntoDM_000021s OntoDM_000021 OntoDM_000021 OntoDM_000021 complex constraint OntoDM_000022 OntoDM_000022s OntoDM_000022 OntoDM_000022 OntoDM_000022 pattern quality OntoDM_000027 OntoDM_000027s OntoDM_000027 OntoDM_000027 OntoDM_000027 output datatype specification is a datatype specification that denotes the datatype of the data on the output part of the dataset. The output data is used for predictive modeling tasks. OntoDM_000027 output data specification OntoDM_000029 OntoDM_000029s OntoDM_000029 OntoDM_000029 OntoDM_000029 predictive model evaluation function specification OntoDM_000031 OntoDM_000031s OntoDM_000031 OntoDM_000031 OntoDM_000031 a dataset specification is a data item specification about a dataset defined with a datatype specification of the data examples aggregated in the dataset. OntoDM_000031 dataset specification OntoDM_000033 OntoDM_000033s OntoDM_000033 OntoDM_000033 OntoDM_000033 data mining algorithm execution OntoDM_000034 OntoDM_000034s OntoDM_000034 OntoDM_000034 OntoDM_000034 Generalization specification of a directive informational entity class that specifies the type of generalization. It includes information about the types of data used to produced the generalization and the language in which the generalization is expressed. OntoDM_000034 represents class of generalizations as in Dzeroski paper OntoDM_000034 generalization specification OntoDM_000036 OntoDM_000036s OntoDM_000036 OntoDM_000036 OntoDM_000036 A predictive model M for types Td and Tc is a function that takes an object of type Td and returns and object of type Tc, i.e. has the signature m::Td-->Tc. OntoDM_000036 predictive model OntoDM_000037 OntoDM_000037s OntoDM_000037 OntoDM_000037 OntoDM_000037 A ensemble of generalizations specification is a generalization specification and denotes a type of generalization that is produced by a ensemble algorithm. This algorithm produces a set of generalizations at its output. OntoDM_000037 ensemble specification OntoDM_000038 OntoDM_000038s OntoDM_000038 OntoDM_000038 OntoDM_000038 A data mining algorithm is an algorithm that solves a data mining task and as a results outputs a generalization. It is usually published in some document (journal publication or technical report). OntoDM_000038 data mining algorithm OntoDM_000039 OntoDM_000039 OntoDM_000039 OntoDM_000039s OntoDM_000039 OntoDM_000039ed OntoDM_000039 has_data_items_number OntoDM_000041 OntoDM_000041s OntoDM_000041 OntoDM_000041 OntoDM_000041 bootstrap sampling process OntoDM_000042 OntoDM_000042s OntoDM_000042 OntoDM_000042 OntoDM_000043 OntoDM_000043s OntoDM_000043 OntoDM_000043 OntoDM_000043 subsumtion based laguage constraint OntoDM_000044 OntoDM_000044s OntoDM_000044 OntoDM_000044 OntoDM_000044 public available resource OntoDM_000047 OntoDM_000047s OntoDM_000047 OntoDM_000047 OntoDM_000048 OntoDM_000048s OntoDM_000048 OntoDM_000048 OntoDM_000048 generalization interpreter specification OntoDM_000049 OntoDM_000049s OntoDM_000049 OntoDM_000049 OntoDM_000050 OntoDM_000050s OntoDM_000050 OntoDM_000050 OntoDM_000050 An ensemble of generalizations is a realizable entity that is a concretization of an ensemble specification and it is a specified output of a data mining algorithm execution. OntoDM_000050 ensemble of generalizations OntoDM_000051 OntoDM_000051s OntoDM_000051 OntoDM_000051 OntoDM_000051 predictive model quality OntoDM_000052 OntoDM_000052s OntoDM_000052 OntoDM_000052 OntoDM_000052 mathematical function specification OntoDM_000053 OntoDM_000053s OntoDM_000053 OntoDM_000053 OntoDM_000053 a fold is a dataset that is an output of a cross validation sampling process. OntoDM_000053 fold OntoDM_000055 OntoDM_000055 OntoDM_000055 OntoDM_000055s OntoDM_000055 OntoDM_000055ed OntoDM_000055 has_description OntoDM_000059 OntoDM_000059s OntoDM_000059 OntoDM_000059 OntoDM_000059 status OntoDM_000063 OntoDM_000063s OntoDM_000063 OntoDM_000063 OntoDM_000063 probability distribution quality OntoDM_000068 OntoDM_000068 OntoDM_000068 OntoDM_000068s OntoDM_000068 OntoDM_000068ed OntoDM_000068 has_features_number OntoDM_000069 OntoDM_000069s OntoDM_000069 OntoDM_000069 OntoDM_000069 optimization evaluation constraint OntoDM_000070 OntoDM_000070s OntoDM_000070 OntoDM_000070 OntoDM_000070 An evaluation datum is a data item that is a specified output of generalization evaluation process. OntoDM_000070 evaluation datum OntoDM_000071 OntoDM_000071s OntoDM_000071 OntoDM_000071 OntoDM_000071 representation OntoDM_000072 OntoDM_000072s OntoDM_000072 OntoDM_000072 OntoDM_000072 Clustering algorithm is a data mining algorithm that solves a clustering task and as a result produces a clustering. OntoDM_000072 clustering algorithm OntoDM_000074 OntoDM_000074 OntoDM_000074 OntoDM_000074s OntoDM_000074 OntoDM_000074ed OntoDM_000074 has_name OntoDM_000075 OntoDM_000075s OntoDM_000075 OntoDM_000075 OntoDM_000076 OntoDM_000076s OntoDM_000076 OntoDM_000076 OntoDM_000076 A single generalization is a realizable entity that is a concretization of a single generalization specification and it is a specified output of a data mining algorithm execution. OntoDM_000076 single generalization OntoDM_000078 OntoDM_000078s OntoDM_000078 OntoDM_000078 OntoDM_000078 A patten specification is a single generalization specification and denotes a pattern P on type T which is a boolean funtion on objects of type T. OntoDM_000078 pattern specification OntoDM_000081 OntoDM_000081s OntoDM_000081 OntoDM_000081 OntoDM_000081 mean_squared_error OntoDM_000084 OntoDM_000084s OntoDM_000084 OntoDM_000084 OntoDM_000084 generalization evaluation OntoDM_000085 OntoDM_000085s OntoDM_000085 OntoDM_000085 OntoDM_000085 predictive_modeling_algorithm_implementation OntoDM_000087 OntoDM_000087s OntoDM_000087 OntoDM_000087 OntoDM_000087 The task of estimating the joint probability distribution involves constructing a probability disctribution given a set of data of a certain datatype. OntoDM_000087 batch probability distribution estimation task OntoDM_000088 OntoDM_000088 OntoDM_000088 OntoDM_000088s OntoDM_000088 OntoDM_000088ed OntoDM_000088 has_number OntoDM_000089 OntoDM_000089s OntoDM_000089 OntoDM_000089 OntoDM_000089 language cost function constraint OntoDM_000092 OntoDM_000092s OntoDM_000092 OntoDM_000092 OntoDM_000092 The task of pattern discovery is to find all local patterns from a given pattern language that satisfy the required conditions. OntoDM_000092 batch pattern discovery task OntoDM_000095 OntoDM_000095s OntoDM_000095 OntoDM_000095 OntoDM_000096 OntoDM_000096s OntoDM_000096 OntoDM_000096 OntoDM_000098 OntoDM_000098s OntoDM_000098 OntoDM_000098 OntoDM_000098 A data mining task is an objective specification that specifies the objective that a data mining algorithm needs to achieve when executed on a dataset to produce as output a generalization. OntoDM_000098 data mining task OntoDM_000099 OntoDM_000099s OntoDM_000099 OntoDM_000099 OntoDM_000099 query specification OntoDM_000100 OntoDM_000100s OntoDM_000100 OntoDM_000100 OntoDM_000100 discreteness OntoDM_000101 OntoDM_000101s OntoDM_000101 OntoDM_000101 OntoDM_000101 soft language cost function constraint OntoDM_000102 OntoDM_000102s OntoDM_000102 OntoDM_000102 OntoDM_000102 probability distribution execution OntoDM_000103 OntoDM_000103s OntoDM_000103 OntoDM_000103 OntoDM_000103 hard language cost function constraint OntoDM_000105 OntoDM_000105s OntoDM_000105 OntoDM_000105 OntoDM_000105 distance OntoDM_000106 OntoDM_000106s OntoDM_000106 OntoDM_000106 OntoDM_000106 Scoring functions often combine evaluation functions and language cost functions. OntoDM_000106 scoring function OntoDM_000107 OntoDM_000107s OntoDM_000107 OntoDM_000107 OntoDM_000107 penalty function OntoDM_000109 OntoDM_000109s OntoDM_000109 OntoDM_000109 OntoDM_000109 sampling_objective OntoDM_000111 OntoDM_000111s OntoDM_000111 OntoDM_000111 OntoDM_000112 OntoDM_000112s OntoDM_000112 OntoDM_000112 OntoDM_000112 Clustering specification is a single generalization specification is a generalization specification and denotes a type of generalization that models the mapping of a set of objects S of type T from S to a set of natural numbers {1,...K}. This generalization is obtained by appliing a clustering algorithm on a set of data. OntoDM_000112 clustering specification OntoDM_000114 OntoDM_000114s OntoDM_000114 OntoDM_000114 OntoDM_000118 OntoDM_000118s OntoDM_000118 OntoDM_000118 OntoDM_000118 Defines properties of a generalization OntoDM_000118 generalization quality OntoDM_000121 OntoDM_000121s OntoDM_000121 OntoDM_000121 OntoDM_000121 a bag is a dataset that is an output of a bootstrap sampling process OntoDM_000121 bag OntoDM_000122 OntoDM_000122s OntoDM_000122 OntoDM_000122 OntoDM_000122 Function that is used to combine other functions OntoDM_000122 combining function OntoDM_000125 OntoDM_000125s OntoDM_000125 OntoDM_000125 OntoDM_000125 integrated_average_squared_error OntoDM_000127 OntoDM_000127s OntoDM_000127 OntoDM_000127 OntoDM_000127 clustering algorithm execution OntoDM_000128 OntoDM_000128s OntoDM_000128 OntoDM_000128 OntoDM_000128 a feature set specification is a data item specification that specifies a set of features used in a specific data mining task. OntoDM_000128 feature set specification OntoDM_000130 OntoDM_000130s OntoDM_000130 OntoDM_000130 OntoDM_000130 A parameter is a quality of an algorithm implementation,and it refers to one of the pieces of data provided as input to the algorithm implementation. The parameter influences the flow of the execution of algorithm realized by a data mining operator that has information about the specific parameter setting used in the execution process . OntoDM_000130 parameter OntoDM_000132 OntoDM_000132s OntoDM_000132 OntoDM_000132 OntoDM_000132 A background knowledge is a data item that is input to data mining algorithm along with the data examples, and represents domain information that is essential to proper functioning of an algorithm. OntoDM_000132 Background knowledge can be seen as a set of mappings that generate new features bi:T-->Ti. OntoDM_000132 background knowledge OntoDM_000134 OntoDM_000134s OntoDM_000134 OntoDM_000134 OntoDM_000134 clustering_algorithm_implementation OntoDM_000136 OntoDM_000136 OntoDM_000136 OntoDM_000136s OntoDM_000136 OntoDM_000136ed OntoDM_000136 has_identifyer OntoDM_000137 OntoDM_000137s OntoDM_000137 OntoDM_000137 OntoDM_000137 constraint specification OntoDM_000138 feature-based data example OntoDM_000139 feature-based DM-dataset OntoDM_000140 OntoDM_000140s OntoDM_000140 OntoDM_000140 OntoDM_000140 dataset representation OntoDM_000142 OntoDM_000142s OntoDM_000142 OntoDM_000142 OntoDM_000142 aggregate function OntoDM_000143 OntoDM_000143s OntoDM_000143 OntoDM_000143 OntoDM_000143 dataset sampling OntoDM_000144 OntoDM_000144s OntoDM_000144 OntoDM_000144 OntoDM_000144 Dataset is an aggregate of data examples. OntoDM_000144 DM-dataset OntoDM_000145 OntoDM_000145s OntoDM_000145 OntoDM_000145 OntoDM_000145 A constant value of some property of a generalization. It is used to define constraints. OntoDM_000145 constraint threshold OntoDM_000149 OntoDM_000149s OntoDM_000149 OntoDM_000149 OntoDM_000149 holdout sampling process OntoDM_000155 OntoDM_000155s OntoDM_000155 OntoDM_000155 OntoDM_000155 frequency OntoDM_000160 OntoDM_000160s OntoDM_000160 OntoDM_000160 OntoDM_000160 Predictive modeling algorithm is a data mining algorithm that solves a predictive modeling task and as a result produces a predictive model. OntoDM_000160 predictive modeling algorithm OntoDM_000162 OntoDM_000162 OntoDM_000162 OntoDM_000162s OntoDM_000162 OntoDM_000162ed OntoDM_000162 is-labeled OntoDM_000167 OntoDM_000167s OntoDM_000167 OntoDM_000167 OntoDM_000167 pattern set execution OntoDM_000168 OntoDM_000168s OntoDM_000168 OntoDM_000168 OntoDM_000168 A data specifcation is-a specifcation entity that specifies on which concrete part of the data the datatype applies to. OntoDM_000168 data specification OntoDM_000170 OntoDM_000170s OntoDM_000170 OntoDM_000170 OntoDM_000170 optimization language cost function constraint OntoDM_000172 OntoDM_000172s OntoDM_000172 OntoDM_000172 OntoDM_000172 The output data type should be the same as the input datatype because IQ asc as filters This should be put as a axiom or rule in PROLOG OntoDM_000172 inductive query OntoDM_000173 OntoDM_000173s OntoDM_000173 OntoDM_000173 OntoDM_000173 The language cost functions are used in language constraints concern the data part of generalizations. Most often they are related to the size/complexity of generalizations. OntoDM_000173 language cost function OntoDM_000175 OntoDM_000175s OntoDM_000175 OntoDM_000175 OntoDM_000175 predictive modelling algorithm execution OntoDM_000178 OntoDM_000178s OntoDM_000178 OntoDM_000178 OntoDM_000178 A distance function d for type T is a mapping from apirs of objects of type T to non negative reals. It has to satisfy 3 main properties OntoDM_000178 distance function OntoDM_000179 OntoDM_000179s OntoDM_000179 OntoDM_000179 OntoDM_000179 An pattern set ensemble is a realizable entity that is a concretization of a pattern set ensemble specification and it is a specified output of a pattern discovery algorithm execution. OntoDM_000179 pattern set ensemble OntoDM_000182 OntoDM_000182s OntoDM_000182 OntoDM_000182 OntoDM_000185 OntoDM_000185s OntoDM_000185 OntoDM_000185 OntoDM_000185 separate test set evaluation process OntoDM_000191 OntoDM_000191s OntoDM_000191 OntoDM_000191 OntoDM_000191 database OntoDM_000192 OntoDM_000192s OntoDM_000192 OntoDM_000192 OntoDM_000192 Pattern discovery algorithm is a data mining algorithm that solves a pattern discovery task and as a result produces a set of patterns. OntoDM_000192 pattern discovery algorithm OntoDM_000195 OntoDM_000195s OntoDM_000195 OntoDM_000195 OntoDM_000195 training set evaluation process OntoDM_000196 OntoDM_000196s OntoDM_000196 OntoDM_000196 OntoDM_000196 algorithm_component_specification OntoDM_000197 OntoDM_000197s OntoDM_000197 OntoDM_000197 OntoDM_000197 data creation query OntoDM_000199 OntoDM_000199s OntoDM_000199 OntoDM_000199 OntoDM_000199 cost function OntoDM_000200 OntoDM_000200s OntoDM_000200 OntoDM_000200 OntoDM_000200 prototype function OntoDM_000201 OntoDM_000201s OntoDM_000201 OntoDM_000201 OntoDM_000201 not_discrete OntoDM_000207 OntoDM_000207s OntoDM_000207 OntoDM_000207 OntoDM_000207 discrete OntoDM_000209 OntoDM_000209s OntoDM_000209 OntoDM_000209 OntoDM_000209 data transformation with generalization query OntoDM_000212 OntoDM_000212s OntoDM_000212 OntoDM_000212 OntoDM_000212 language constraint OntoDM_000214 OntoDM_000214s OntoDM_000214 OntoDM_000214 OntoDM_000214 draft OntoDM_000219 OntoDM_000219s OntoDM_000219 OntoDM_000219 OntoDM_000219 clustering evaluation function specification OntoDM_000223 OntoDM_000223s OntoDM_000223 OntoDM_000223 OntoDM_000223 clustering representation OntoDM_000224 OntoDM_000224s OntoDM_000224 OntoDM_000224 OntoDM_000224 generalization selection query OntoDM_000228 OntoDM_000228s OntoDM_000228 OntoDM_000228 OntoDM_000228 predictive model specification is a single generalization specification and denotes a type of generalization that represents a mapping taking objects of type Td and returns objects of type To. OntoDM_000228 predictive model specification OntoDM_000229 OntoDM_000229s OntoDM_000229 OntoDM_000229 OntoDM_000229 data mining algorithm implementation OntoDM_000232 OntoDM_000232s OntoDM_000232 OntoDM_000232 OntoDM_000233 OntoDM_000233s OntoDM_000233 OntoDM_000233 OntoDM_000233 full fledged query OntoDM_000234 OntoDM_000234s OntoDM_000234 OntoDM_000234 OntoDM_000234 clustering execution OntoDM_000235 OntoDM_000235s OntoDM_000235 OntoDM_000235 OntoDM_000235 In the task of predictive modeling, we are given a dataset that consists of examples of the form (d,o), where d is of type Td and each o is of type To. To learn a predictive model means to find a mapping from description to output m::Td--->To that fits data closly. OntoDM_000235 predictive modelling task OntoDM_000237 OntoDM_000237s OntoDM_000237 OntoDM_000237 OntoDM_000237 soft evaluation constraint OntoDM_000238 OntoDM_000238s OntoDM_000238 OntoDM_000238 OntoDM_000239 OntoDM_000239s OntoDM_000239 OntoDM_000239 OntoDM_000239 data selection query OntoDM_000243 OntoDM_000243s OntoDM_000243 OntoDM_000243 OntoDM_000243 example weight OntoDM_000247 OntoDM_000247s OntoDM_000247 OntoDM_000247 OntoDM_000247 Descriptive datatype specification is a data item specification that denotes the datatype of the data from the descriptive part of a dataset. OntoDM_000247 descriptive data specification OntoDM_000249 OntoDM_000249s OntoDM_000249 OntoDM_000249 OntoDM_000249 evaluation constraint OntoDM_000250 OntoDM_000250s OntoDM_000250 OntoDM_000250 OntoDM_000250 pattern representation OntoDM_000253 OntoDM_000253s OntoDM_000253 OntoDM_000253 OntoDM_000253 Probability distribution estimation algorithm is a data mining algorithm that solves a probability distribution estimation task and as a result produces a probability distribution. OntoDM_000253 probability distribution estimation algorithm OntoDM_000255 OntoDM_000255s OntoDM_000255 OntoDM_000255 OntoDM_000255 cross validation sampling process OntoDM_000256 OntoDM_000256s OntoDM_000256 OntoDM_000256 OntoDM_000256 generalization representation OntoDM_000258 OntoDM_000258s OntoDM_000258 OntoDM_000258 OntoDM_000258 background knowledge representation OntoDM_000259 OntoDM_000259s OntoDM_000259 OntoDM_000259 OntoDM_000259 pattern discovery evaluation function specification OntoDM_000260 OntoDM_000260s OntoDM_000260 OntoDM_000260 OntoDM_000260 N fold validation evaluation process OntoDM_000261 OntoDM_000261s OntoDM_000261 OntoDM_000261 OntoDM_000261 knowledge discovery scenario objective OntoDM_000264 OntoDM_000264s OntoDM_000264 OntoDM_000264 OntoDM_000264 The task of clustering in general is concerned with grouping objects into classes of similar objects. Given a set of examples (object descriptions), the task of clustering is to partition these examples into subsets, called clusters. OntoDM_000264 batch clustering task OntoDM_000265 OntoDM_000265s OntoDM_000265 OntoDM_000265 OntoDM_000265 probability_distribution_estimation_algorithm_implementation OntoDM_000266 OntoDM_000266s OntoDM_000266 OntoDM_000266 OntoDM_000266 selection composition of generalizations query OntoDM_000269 OntoDM_000269s OntoDM_000269 OntoDM_000269 OntoDM_000269 train set role OntoDM_000270 OntoDM_000270s OntoDM_000270 OntoDM_000270 OntoDM_000270 primitive constraint OntoDM_000271 OntoDM_000271s OntoDM_000271 OntoDM_000271 OntoDM_000271 probability distribution evaluation function specification OntoDM_000273 OntoDM_000273s OntoDM_000273 OntoDM_000273 OntoDM_000273 generalizations and data from generalizations query OntoDM_000276 OntoDM_000276s OntoDM_000276 OntoDM_000276 OntoDM_000276 hard evaluation constraint OntoDM_000280 OntoDM_000280s OntoDM_000280 OntoDM_000280 OntoDM_000280 predictive model representation OntoDM_000282 OntoDM_000282s OntoDM_000282 OntoDM_000282 OntoDM_000282 predictive model evaluation OntoDM_000285 OntoDM_000285s OntoDM_000285 OntoDM_000285 OntoDM_000285 A probability distribution specification is a single generalization specification and denotes a type of generalization that is a mapping from objects of type T to non-negative reals. This generalization is an output of a probability distribution estimation algorithm. OntoDM_000285 probability distribution specification OntoDM_000287 OntoDM_000287s OntoDM_000287 OntoDM_000287 OntoDM_000287 pattern discovery algorithm execution OntoDM_000288 OntoDM_000288s OntoDM_000288 OntoDM_000288 OntoDM_000288 pattern_discovery_algorithm_implementation OntoDM_000290 OntoDM_000290s OntoDM_000290 OntoDM_000290 OntoDM_000291 OntoDM_000291s OntoDM_000291 OntoDM_000291 OntoDM_000291 refinement operator OntoDM_000293 OntoDM_000293s OntoDM_000293 OntoDM_000293 OntoDM_000293 intra_cluster_variance OntoDM_000294 OntoDM_000294s OntoDM_000294 OntoDM_000294 OntoDM_000294 probability_distribution_scoring_function OntoDM_000297 OntoDM_000297s OntoDM_000297 OntoDM_000297 OntoDM_000297 generalization execution OntoDM_000298 OntoDM_000298s OntoDM_000298 OntoDM_000298 OntoDM_000298 A feature specification is a data item specification that specifies a primitive data feature (belonging to a primitive datatype), used in propositional data mining where the descriptive datatype is usually a tuple of primitive features. OntoDM_000298 feature specification OntoDM_000299 OntoDM_000299s OntoDM_000299 OntoDM_000299 OntoDM_000300 OntoDM_000300s OntoDM_000300 OntoDM_000300 OntoDM_000300 generalizations and data from data query OntoDM_000302 OntoDM_000302s OntoDM_000302 OntoDM_000302 OntoDM_000302 optimization function OntoDM_000306 OntoDM_000306s OntoDM_000306 OntoDM_000306 OntoDM_000306 probability distribution estimation algorithm execution OntoDM_000309 OntoDM_000309s OntoDM_000309 OntoDM_000309 OntoDM_000309 predictive model execution OntoDM_000311 OntoDM_000311 OntoDM_000311 OntoDM_000311s OntoDM_000311 OntoDM_000311ed OntoDM_000311 has_URL OntoDM_000316 OntoDM_000316s OntoDM_000316 OntoDM_000316 OntoDM_000316 A clustering C on a set of objects S of type T is a function from S to {1,...,k} where k is the number of clusters. OntoDM_000316 clustering OntoDM_000333 OntoDM_000333s OntoDM_000333 OntoDM_000333 OntoDM_000333 itemized list OntoDM_000343 OntoDM_000343s OntoDM_000343 OntoDM_000343 OntoDM_000343 A generalization is a realizable entity that is a concretization of a generalization specification and it is a specified output of a data mining algorithm execution. OntoDM_000343 generalization OntoDM_001019 OntoDM_001019s OntoDM_001019 OntoDM_001019 OntoDM_001019 tuple output specification OntoDM_001020 OntoDM_001020s OntoDM_001020 OntoDM_001020 OntoDM_001020 tuple of booleanORdiscrete output specification OntoDM_001021 OntoDM_001021s OntoDM_001021 OntoDM_001021 OntoDM_001021 set output specification output specification OntoDM_001022 OntoDM_001022s OntoDM_001022 OntoDM_001022 OntoDM_001022 booleanORdiscrete output data specification OntoDM_001334 OntoDM_001334s OntoDM_001334 OntoDM_001334 OntoDM_001334 Regression algorithm is a data mining algorithm that solves a regression task and as a result produces a regression model. OntoDM_001334 regression algorithm OntoDM_003932 OntoDM_003932s OntoDM_003932 OntoDM_003932 OntoDM_003932 A predictive model for primitive output is a predictive model specification and denotes predictive models that are built on data having primitive datatype on the output part. OntoDM_003932 predictive model for primitive output OntoDM_004843 OntoDM_004843s OntoDM_004843 OntoDM_004843 OntoDM_004843 dataset role OntoDM_020420 OntoDM_020420s OntoDM_020420 OntoDM_020420 OntoDM_020420 LCPN DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_020420 LCPN DAG based HC algorithm OntoDM_036834 OntoDM_036834s OntoDM_036834 OntoDM_036834 OntoDM_036834 LCPN tree-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_036834 LPCN tree based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_038162 OntoDM_038162s OntoDM_038162 OntoDM_038162 OntoDM_038162 data mining operator OntoDM_039503 OntoDM_039503s OntoDM_039503 OntoDM_039503 OntoDM_039503 A binary classification model is a classification model and denotes predictive models that are built on data having a boolean datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_039503 binary classification model OntoDM_056700 OntoDM_056700s OntoDM_056700 OntoDM_056700 OntoDM_056700 DAG based hierarchical classification task with multiple label paths and full depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and all instances have full depth of labeling. OntoDM_056700 DAG based hierarchical classification task with multiple label paths and full depth labeling OntoDM_057491 OntoDM_057491s OntoDM_057491 OntoDM_057491 OntoDM_057491 DAG based hierarchical classification task with single label paths is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels. OntoDM_057491 DAG based hierarchical classification task with single label paths OntoDM_063973 OntoDM_063973s OntoDM_063973 OntoDM_063973 OntoDM_063973 DAG-based hierarchical classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a labeled DAG datatype with boolean edges and discrete nodes on the output part of the data. OntoDM_063973 DAG-based hierarchical classification dataset OntoDM_067028 OntoDM_067028s OntoDM_067028 OntoDM_067028 OntoDM_067028 Multi-target binary classification model is a multi-target classification model and denotes predictive models that are built on data having a tuple of boolean (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_067028 multi-target binary classification model OntoDM_071450 OntoDM_071450s OntoDM_071450 OntoDM_071450 OntoDM_071450 LCL DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_071450 LCL DAG based HC algorithm OntoDM_078701 OntoDM_078701s OntoDM_078701 OntoDM_078701 OntoDM_078701 transactional dataset is a dataset specification of unlabeled dataset with a set of discrete datatype on the descriptive part of the data. OntoDM_078701 transactional dataset OntoDM_081165 OntoDM_081165s OntoDM_081165 OntoDM_081165 OntoDM_081165 LCN tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_081165 LCN tree based HC algorithm for multiple path prediction OntoDM_086891 OntoDM_086891s OntoDM_086891 OntoDM_086891 OntoDM_086891 LCL DAG-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_086891 LCL DAG based HC algorithm for single path and mandatory leaf node prediction OntoDM_099826 OntoDM_099826s OntoDM_099826 OntoDM_099826 OntoDM_099826 predictive models ensemble specification is a ensemble specification and denotes a type of generalization that is produced by a predictive model ensemble algorithm. This algorithm produces a collection of predictive models at its output. OntoDM_099826 predictive models ensemble specification OntoDM_10000000135 data stream OntoDM_10000000136 data stream example OntoDM_10000000137 data stream representation OntoDM_10000000138 data stream quality OntoDM_101020 OntoDM_101020s OntoDM_101020 OntoDM_101020 OntoDM_101020 tag OntoDM_104594 OntoDM_104594s OntoDM_104594 OntoDM_104594 OntoDM_104594 LCN DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_104594 LCN DAG based HC algorithm for single path prediction OntoDM_107836 OntoDM_107836s OntoDM_107836 OntoDM_107836 OntoDM_107836 Hierarchical classification model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a labeled graph datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_107836 hierarchical classification model OntoDM_108244 OntoDM_108244s OntoDM_108244 OntoDM_108244 OntoDM_108244 Multi-class classification is a data mining algorithm that solves a multi-class classification task and as a result produces a multi-class classification model. OntoDM_108244 multi-class classification algorithm OntoDM_108452 OntoDM_108452s OntoDM_108452 OntoDM_108452 OntoDM_108452 GC DAG based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_112569 OntoDM_112569s OntoDM_112569 OntoDM_112569 OntoDM_112569 algorithm implementation OntoDM_112772 OntoDM_112772s OntoDM_112772 OntoDM_112772 OntoDM_112772 A classification model is a feature-based predictive model for primitive output and denotes predictive models that are built on data having boolean or discrete datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_112772 classification model OntoDM_114697 OntoDM_114697s OntoDM_114697 OntoDM_114697 OntoDM_114697 Multi-target prediction task is a feature-based structured output prediction task where the objective is to predict a set of features (represented with a output tuple of primitives datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_114697 supervised multi-target prediction task OntoDM_135761 OntoDM_135761s OntoDM_135761 OntoDM_135761 OntoDM_135761 GC DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach. OntoDM_135761 GC DAG based HC algorithm for single path prediction OntoDM_136533 OntoDM_136533s OntoDM_136533 OntoDM_136533 OntoDM_136533 Multi-target regression tree is a multi-target regression model and denotes predictive models that are built on data having a tuple of reals (set of output features) on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and is expressed in the language of decision trees. OntoDM_136533 multi-target regression tree OntoDM_139240 OntoDM_139240s OntoDM_139240 OntoDM_139240 OntoDM_139240 scenario title OntoDM_139258 OntoDM_139258s OntoDM_139258 OntoDM_139258 OntoDM_139258 Multi-label decision tree is a multi-label classification model and denotes predictive models that are built on data having a set of discrete datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees. OntoDM_139258 multi-label decision tree OntoDM_145996 OntoDM_145996s OntoDM_145996 OntoDM_145996 OntoDM_145996 output tuple of boolean specification is an output tuple of primitives specification that denotes a tuple of boolean datatype on the output part of the data. OntoDM_145996 output:tuple of boolean OntoDM_147247 OntoDM_147247s OntoDM_147247 OntoDM_147247 OntoDM_147247 algorithm execution OntoDM_150825 OntoDM_150825s OntoDM_150825 OntoDM_150825 OntoDM_150825 output discrete specification is an output primitive datatype specification that denotes a discrete datatype on the output part of the data. OntoDM_150825 output:discrete OntoDM_153542 OntoDM_153542s OntoDM_153542 OntoDM_153542 OntoDM_153542 output set of reals specification is an output set specification that denotes a set of reals datatype on the output part of the data. OntoDM_153542 output:set of reals OntoDM_155699 OntoDM_155699s OntoDM_155699 OntoDM_155699 OntoDM_155699 Binary classification is a data mining algorithm that solves a binary classification task and as a result produces a binary classification model. OntoDM_155699 binary classification algorithm OntoDM_157687 OntoDM_157687s OntoDM_157687 OntoDM_157687 OntoDM_157687 A structure-based predictive model for structured output is a predictive model specification and denotes predictive models that are built on data having structured datatype on the output part, and a structured datatype on the descriptive part. OntoDM_157687 structure-based predictive model for structured output OntoDM_165764 OntoDM_165764s OntoDM_165764 OntoDM_165764 OntoDM_165764 A feature-based predictive model for structured output is a predictive model specification and denotes predictive models that are built on data having a structured datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_165764 feature-based predictive model for structured output OntoDM_169891 OntoDM_169891s OntoDM_169891 OntoDM_169891 OntoDM_169891 unlabeled structure-based dataset is a dataset specification of unlabeled dataset with a structured descriptive datatype specification. OntoDM_169891 structure-based unlabeled dataset OntoDM_171335 OntoDM_171335 OntoDM_171335 C OntoDM_171503 OntoDM_171503s OntoDM_171503 OntoDM_171503 OntoDM_171503 Flat classification algorithm is a data mining algorithm that solves a classification task and as a result produces a classification model. OntoDM_171503 flat classification algorithm OntoDM_174112 OntoDM_174112s OntoDM_174112 OntoDM_174112 OntoDM_174112 LCPN tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_174112 LPCN tree based HC algorithm for multiple path prediction OntoDM_180345 OntoDM_180345s OntoDM_180345 OntoDM_180345 OntoDM_180345 LCN DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_180345 LCN DAG based HC algorithm for multiple path prediction OntoDM_182357 OntoDM_182357s OntoDM_182357 OntoDM_182357 OntoDM_182357 data mining scenario OntoDM_191108 OntoDM_191108s OntoDM_191108 OntoDM_191108 OntoDM_191108 output real specification is an output primitive datatype specification that denotes a real datatype on the output part of the data. OntoDM_191108 output:real OntoDM_201436 OntoDM_201436s OntoDM_201436 OntoDM_201436 OntoDM_201436 parameter specification is a specification entity that is about a parameter. OntoDM_201436 parameter specification OntoDM_205203 OntoDM_205203s OntoDM_205203 OntoDM_205203 OntoDM_205203 Feature-based primitive output prediction task is a predicting primitive output task where the objective is to predict a primitive output (represented with a primitive datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_205203 supervised feature-based primitive output prediction task OntoDM_206736 OntoDM_206736 OntoDM_206736 JAVA OntoDM_206736 OntoDM_206736 OntoDM_207566 OntoDM_207566s OntoDM_207566 OntoDM_207566 OntoDM_207566 A feature-based predictive model for primitive output is a predictive model specification and denotes predictive models that are built on data having primitive datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_207566 feature-based predictive model for primitive output OntoDM_208012 OntoDM_208012s OntoDM_208012 OntoDM_208012 OntoDM_208012 Time-series prediction task is a feature-based structured output prediction task where the objective is to predict a time series (represented with a output sequence of real datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_208012 supervised time-series prediction task OntoDM_208659 OntoDM_208659s OntoDM_208659 OntoDM_208659 OntoDM_208659 binary classification task is a flat classification task where the objective is to predict a boolean output (represented with a boolean datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_208659 supervised binary classification task OntoDM_213136 OntoDM_213136s OntoDM_213136 OntoDM_213136 OntoDM_213136 GC DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a global classifier (GC) approach. OntoDM_213136 GC DAG based HC algorithm OntoDM_221045 OntoDM_221045s OntoDM_221045 OntoDM_221045 OntoDM_221045 LCN DAG-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_221045 LCPN DAG based HC algorithm for single path and mandatory leaf node prediction OntoDM_225681 OntoDM_225681s OntoDM_225681 OntoDM_225681 OntoDM_225681 Multi-target prediction model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a tuple of primitives (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_225681 multi-target prediction model OntoDM_231548 OntoDM_231548s OntoDM_231548 OntoDM_231548 OntoDM_231548 Multi-target binary classification task is a multi-target classification task where the objective is to predict a set of binary features (represented with a output tuple of booleans ) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_231548 supervised multi-target binary classification OntoDM_232231 OntoDM_232231s OntoDM_232231 OntoDM_232231 OntoDM_232231 LCPN DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_232231 LCPN DAG based HC algorithm for multiple path prediction OntoDM_242219 OntoDM_242219s OntoDM_242219 OntoDM_242219 OntoDM_242219 predicting primitive output task is a predictive modeling task where the objective is to predict a primitive output (represented with a primitive datatype). OntoDM_242219 supervised primitive output prediction task OntoDM_246002 OntoDM_246002s OntoDM_246002 OntoDM_246002 OntoDM_246002 descriptive structured data specification is a descriptive datatype specification that denotes a structured datatype on the descriptive part of the data. OntoDM_246002 structure-based descriptive data specification OntoDM_246907 OntoDM_246907s OntoDM_246907 OntoDM_246907 OntoDM_246907 Tree based hierarchical classification task with single label paths and partial depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and at least one instance has a partial depth of labeling. OntoDM_246907 tree-based hierarchical classification task with single label paths and partial depth labeling OntoDM_247241 OntoDM_247241s OntoDM_247241 OntoDM_247241 OntoDM_247241 Multi-label classification model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a set of discrete datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_247241 multi-label classification model OntoDM_247510 OntoDM_247510s OntoDM_247510 OntoDM_247510 OntoDM_247510 output set specification is an output structured datatype specification that denotes a set datatype on the output part of the data. OntoDM_247510 output:set of primitives OntoDM_251508 OntoDM_251508s OntoDM_251508 OntoDM_251508 OntoDM_251508 LCPN tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_251508 LPCN tree based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_257039 OntoDM_257039s OntoDM_257039 OntoDM_257039 OntoDM_257039 flat classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a boolean or discrete datatype on the output part of the data (specified with the feature specification). OntoDM_257039 flat classification dataset OntoDM_257366 OntoDM_257366s OntoDM_257366 OntoDM_257366 OntoDM_257366 LCN tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_257366 LCN tree based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_278835 OntoDM_278835s OntoDM_278835 OntoDM_278835 OntoDM_278835 output sequence of discrete specification is an output sequence datatype specification that denotes a sequence of discrete datatype on the output part of the data. OntoDM_278835 output:sequence of discrete OntoDM_279492 OntoDM_279492s OntoDM_279492 OntoDM_279492 OntoDM_279492 output DAG specification is an output labeled graph specification that denotes a DAG datatype on the output part of the data. OntoDM_279492 output:DAG OntoDM_279507 OntoDM_279507s OntoDM_279507 OntoDM_279507 OntoDM_280661 OntoDM_280661s OntoDM_280661 OntoDM_280661 OntoDM_280661 A regression tree is a regression model and denotes predictive models that are built on data having a real datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees. OntoDM_280661 regression tree OntoDM_281640 OntoDM_281640s OntoDM_281640 OntoDM_281640 OntoDM_281640 DAG based hierarchical classification task with single label paths and partial depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and at least one instance has a partial depth of labeling. OntoDM_281640 DAG based hierarchical classification task with single label paths and partial depth labeling OntoDM_285570 OntoDM_285570s OntoDM_285570 OntoDM_285570 OntoDM_285570 workflow execution OntoDM_295724 OntoDM_295724s OntoDM_295724 OntoDM_295724 OntoDM_295724 Structure-based predictive modeling algorithm for structured output is a data mining algorithm that solves a structure-based structured output prediction task and as a result produces a structure-based predictive model for structured output. OntoDM_295724 structure-based predictive modeling algorithm for structured output OntoDM_300937 OntoDM_300937s OntoDM_300937 OntoDM_300937 OntoDM_300937 GC tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach. OntoDM_300937 GC tree based HC algorithm for single path prediction OntoDM_308048 OntoDM_308048s OntoDM_308048 OntoDM_308048 OntoDM_308048 hierarchical classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a labeled tree with boolean edges and discrete nodes datatype on the output part of the data. OntoDM_308048 tree-based hierarchical classification dataset OntoDM_311225 OntoDM_311225s OntoDM_311225 OntoDM_311225 OntoDM_311225 DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model. OntoDM_311225 DAG based HC algorithm OntoDM_316568 OntoDM_316568s OntoDM_316568 OntoDM_316568 OntoDM_316568 A binary decision tree is a binary classification model and denotes predictive models that are built on data having a boolean datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees. OntoDM_316568 binary decision tree OntoDM_318626 OntoDM_318626s OntoDM_318626 OntoDM_318626 OntoDM_318626 Pattern set ensemble specification is a ensemble specification and denotes a type of generalization that is produced by a pattern set ensemble algorithm. This algorithm produces a collection of pattern sets at its output. OntoDM_318626 pattern set ensemble specification OntoDM_319168 OntoDM_319168s OntoDM_319168 OntoDM_319168 OntoDM_319168 LCL DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_319168 LCL DAG based HC algorithm for multiple path prediction OntoDM_328701 OntoDM_328701s OntoDM_328701 OntoDM_328701 OntoDM_328701 LCN DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_328701 LCN DAG based HC algorithm OntoDM_330210 OntoDM_330210s OntoDM_330210 OntoDM_330210 OntoDM_330210 specification entity OntoDM_341564 OntoDM_341564s OntoDM_341564 OntoDM_341564 OntoDM_341564 output tuple of primitives specification is an output structured datatype specification that denotes a tuple of primitives datatype on the output part of the data. OntoDM_341564 output:tuple of primitives OntoDM_352467 OntoDM_352467s OntoDM_352467 OntoDM_352467 OntoDM_352467 output tuple of discrete specification is an output tuple of primitives specification that denotes a tuple of discrete datatype on the output part of the data. OntoDM_352467 output:tuple of discrete OntoDM_353154 OntoDM_353154s OntoDM_353154 OntoDM_353154 OntoDM_353154 LCN tree-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_353154 LCN tree based HC algorithm for single path and mandatory leaf node prediction OntoDM_354620 OntoDM_354620s OntoDM_354620 OntoDM_354620 OntoDM_354620 Feature-based structured output prediction task is a predicting structured output task where the objective is to predict a structured output (represented with a output structured datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_354620 supervised feature-based structured output prediction task OntoDM_357792 OntoDM_357792s OntoDM_357792 OntoDM_357792 OntoDM_357792 Random Subspaces OntoDM_361570 OntoDM_361570s OntoDM_361570 OntoDM_361570 OntoDM_361570 data processing algorithm OntoDM_368254 OntoDM_368254s OntoDM_368254 OntoDM_368254 OntoDM_368254 Time-series prediction model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a sequence of reals on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_368254 time series prediction model OntoDM_368779 OntoDM_368779s OntoDM_368779 OntoDM_368779 OntoDM_368779 Predictive modeling ensemble algorithm is a data mining algorithm that solves a predictive modeling task and as a result produces a ensemble of predictive models. OntoDM_368779 predictive modelling ensemble algorithm OntoDM_369100 OntoDM_369100s OntoDM_369100 OntoDM_369100 OntoDM_369100 A multi-class decision tree is a multi-class classification model and denotes predictive models that are built on data having a discrete datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees. OntoDM_369100 multi-class decision tree OntoDM_372340 OntoDM_372340s OntoDM_372340 OntoDM_372340 OntoDM_372340 DAG based hierarchical classification task with multiple label paths is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels. OntoDM_372340 DAG based hierarchical classification task with multiple label paths OntoDM_372936 OntoDM_372936s OntoDM_372936 OntoDM_372936 OntoDM_372936 labeled dataset is a dataset specification for datasets that have both a descriptive and output specification of the data examples contained in the dataset. OntoDM_372936 labeled dataset OntoDM_373964 OntoDM_373964s OntoDM_373964 OntoDM_373964 OntoDM_373964 Tree based hierarchical classification task with multiple label paths and full depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output treewith boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and all instances have full depth of labeling. OntoDM_373964 tree-based hierarchical classification task with multiple label paths and full depth labeling OntoDM_374511 OntoDM_374511s OntoDM_374511 OntoDM_374511 OntoDM_374511 Time-series prediction decision tree is a time-series prediction model and denotes predictive models that are built on data having a sequence of reals on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and is expressed in the language of decision trees. OntoDM_374511 time-series prediction decision tree OntoDM_376810 OntoDM_376810s OntoDM_376810 OntoDM_376810 OntoDM_376810 LCN tree-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_376810 LPCN tree based HC algorithm for single path and mandatory leaf node prediction OntoDM_379153 OntoDM_379153s OntoDM_379153 OntoDM_379153 OntoDM_379153 descriptive tuple of primitives specification is a descriptive datatype specification that denotes a tuple of primitives datatype on the descriptive part of the data. OntoDM_379153 feature-based descriptive data specification OntoDM_382943 OntoDM_382943s OntoDM_382943 OntoDM_382943 OntoDM_382943 Predictive modeling algorithm for primitive output is a data mining algorithm that solves a predicting primitive output task and as a result produces a predictive model for primitive output. OntoDM_382943 predictive modeling algorithm for primitive output OntoDM_387728 OntoDM_387728s OntoDM_387728 OntoDM_387728 OntoDM_387728 flat classification task is a feature-based primitive output task where the objective is to predict a boolean or discrete output (represented with a boolean or discrete datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_387728 supervised flat classification task OntoDM_388961 OntoDM_388961s OntoDM_388961 OntoDM_388961 OntoDM_388961 LCL tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_388961 LCL tree based HC algorithm for multiple path prediction OntoDM_397256 OntoDM_397256 OntoDM_397256 boolean datatype OntoDM_398436 OntoDM_398436s OntoDM_398436 OntoDM_398436 OntoDM_398436 GC tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a global classifier (GC) approach. OntoDM_398436 GC tree-based HC algorithm OntoDM_400000 data specification OntoDM_400001 batch data mining task OntoDM_400002 online data mining task OntoDM_400003 online clustering task OntoDM_400004 online pattern discovery task OntoDM_400005 online probability distribution estimation task OntoDM_408772 OntoDM_408772s OntoDM_408772 OntoDM_408772 OntoDM_408772 LCN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_408772 LCN DAG based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_419418 OntoDM_419418s OntoDM_419418 OntoDM_419418 OntoDM_419418 Tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model. OntoDM_419418 tree-based HC algorithm OntoDM_419460 OntoDM_419460s OntoDM_419460 OntoDM_419460 OntoDM_419460 A single generalization specification is a generalization specification and denotes a type of generalization that is produced by a single generalization data mining algorithm. This algorithm produces only one generalization at its output. OntoDM_419460 single generalization specification OntoDM_419544 OntoDM_419544s OntoDM_419544 OntoDM_419544 OntoDM_419544 information content entity quality OntoDM_429658 OntoDM_429658s OntoDM_429658 OntoDM_429658 OntoDM_429658 Clustering ensemble algorithm is a data mining algorithm that solves a clustering task and as a result produces an ensemble of clusterings. OntoDM_429658 clustering ensemble algorithm OntoDM_433774 OntoDM_433774s OntoDM_433774 OntoDM_433774 OntoDM_433774 language of markov chains OntoDM_452759 OntoDM_452759s OntoDM_452759 OntoDM_452759 OntoDM_452759 Bagging OntoDM_458780 OntoDM_458780 OntoDM_458780 C++ OntoDM_458784 OntoDM_458784s OntoDM_458784 OntoDM_458784 OntoDM_458784 output tree specification is an output labeled graph specification that denotes a tree datatype on the output part of the data. OntoDM_458784 output:tree OntoDM_462575 OntoDM_462575s OntoDM_462575 OntoDM_462575 OntoDM_462575 Time-series predictive modeling algorithm is a data mining algorithm that solves a time-series classification task and as a result produces a time series predictive model. OntoDM_462575 time-series predictive modelling algorithm OntoDM_464319 OntoDM_464319s OntoDM_464319 OntoDM_464319 OntoDM_464319 LCN tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_464319 LCN tree based HC algorithm OntoDM_464484 OntoDM_464484s OntoDM_464484 OntoDM_464484 OntoDM_464484 Probability distribution estimation ensemble algorithm is a data mining algorithm that solves a probablility distribution estimation task and as a result produces a ensemble of probability distributions. OntoDM_464484 probability distribution estimation ensemble algorithm OntoDM_467074 OntoDM_467074s OntoDM_467074 OntoDM_467074 OntoDM_467074 LCPN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_467074 LCPN DAG based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_470460 OntoDM_470460s OntoDM_470460 OntoDM_470460 OntoDM_470460 LCPN tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_470460 LPCN tree based HC algorithm for single path prediction OntoDM_474912 OntoDM_474912s OntoDM_474912 OntoDM_474912 OntoDM_474912 LCL tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_474912 LCL tree based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_478450 OntoDM_478450s OntoDM_478450 OntoDM_478450 OntoDM_478450 workflow OntoDM_481568 OntoDM_481568s OntoDM_481568 OntoDM_481568 OntoDM_481568 Multi-target multi-class classification model is a multi-target classification model and denotes predictive models that are built on data having a tuple of discrete (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_481568 multi-target multi-class classification model OntoDM_485256 OntoDM_485256s OntoDM_485256 OntoDM_485256 OntoDM_485256 GC DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach. OntoDM_485256 GC DAG based HC algorithm for multiple path prediction OntoDM_490530 OntoDM_490530s OntoDM_490530 OntoDM_490530 OntoDM_490530 clusterings ensemble specification is a ensemble specification and denotes a type of generalization that is produced by a clustering ensemble algorithm. This algorithm produces a set of clusterings at its output. OntoDM_490530 clusterings ensemble specification OntoDM_491182 OntoDM_491182s OntoDM_491182 OntoDM_491182 OntoDM_491182 DAG based hierarchical classification task is a hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_491182 supervised DAG based hierarchical classification task OntoDM_495960 OntoDM_495960s OntoDM_495960 OntoDM_495960 OntoDM_495960 Multi-target classification model is a multi-target prediction model and denotes predictive models that are built on data having a tuple of boolean or discrete (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_495960 multi-target classification model OntoDM_506961 OntoDM_506961s OntoDM_506961 OntoDM_506961 OntoDM_506961 Multi-target regression task is a multi-target prediction task where the objective is to predict a set of real features (represented with a output tuple of reals) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_506961 supervised multi-target regression task OntoDM_507935 OntoDM_507935s OntoDM_507935 OntoDM_507935 OntoDM_507935 Hierarchical classification algorithm is a data mining algorithm that solves a hierarchical classification task and as a result produces a hierarchical classification model. OntoDM_507935 hierarchical classification algorithm OntoDM_510504 OntoDM_510504s OntoDM_510504 OntoDM_510504 OntoDM_510504 Multi-label classification task is a feature-based structured output prediction task where the objective is to predict a set of discrete output labels (represented with a output set of discrete datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_510504 supervised multi-label classification task OntoDM_510989 OntoDM_510989s OntoDM_510989 OntoDM_510989 OntoDM_510989 a parameter setting is a data item that is a quality specification of some algorithm parameter and denotes its configuration when an algorithm implementation is used as a data mining operator in the process of algorithm execution. OntoDM_510989 parameter setting OntoDM_512674 OntoDM_512674s OntoDM_512674 OntoDM_512674 OntoDM_512674 LCN tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_512674 LCN tree based HC algorithm for single path prediction OntoDM_516954 OntoDM_516954s OntoDM_516954 OntoDM_516954 OntoDM_516954 output sequence specification is an output structured datatype specification that denotes a sequence datatype on the output part of the data. OntoDM_516954 output:sequence OntoDM_519526 OntoDM_519526s OntoDM_519526 OntoDM_519526 OntoDM_519526 GC tree based HC algorithm for single path and mandatory leaf node prediction OntoDM_528443 OntoDM_528443s OntoDM_528443 OntoDM_528443 OntoDM_528443 LCL tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_528443 LCL tree based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_530819 OntoDM_530819s OntoDM_530819 OntoDM_530819 OntoDM_530819 A ensemble algorithm is a data mining algorithm that solves a data mining task and as a result produces an ensemble of generalizations. OntoDM_530819 ensemble algorithm OntoDM_532190 OntoDM_532190s OntoDM_532190 OntoDM_532190 OntoDM_532190 language of neural nets OntoDM_535716 OntoDM_535716s OntoDM_535716 OntoDM_535716 OntoDM_535716 Probability distributions specification is a ensemble specification and denotes a type of generalization that is produced by a probability distribution estimation ensemble algorithm. This algorithm produces a set of probability distributions at its output. OntoDM_535716 probability distributions ensemble specification OntoDM_537602 OntoDM_537602s OntoDM_537602 OntoDM_537602 OntoDM_537602 labeled feature-based dataset with primitive output is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a primitive datatype on the output part of the data (specified with the output feature specification). OntoDM_537602 feature-based completely labeled dataset with primitive output OntoDM_540049 OntoDM_540049s OntoDM_540049 OntoDM_540049 OntoDM_540049 LC tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier (LC) approach. OntoDM_540049 LC tree based HC algorithm OntoDM_541005 OntoDM_541005s OntoDM_541005 OntoDM_541005 OntoDM_541005 multi-label classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a set of discrete datatype on the output part of the data. OntoDM_541005 multi-label classification dataset OntoDM_542264 OntoDM_542264s OntoDM_542264 OntoDM_542264 OntoDM_542264 Tree based hierarchical classification task is a hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_542264 supervised tree-based hierarchical classification task OntoDM_544234 OntoDM_544234s OntoDM_544234 OntoDM_544234 OntoDM_544234 data mining software toolkit OntoDM_548678 OntoDM_548678s OntoDM_548678 OntoDM_548678 OntoDM_548678 A data mining task description is-a specifcation entity that denotes a specific data mining task applied on a specific dataset, having in mind the specific type of generalization produced by the task (generalization specifcation) OntoDM_548678 data mining task description OntoDM_548720 OntoDM_548720s OntoDM_548720 OntoDM_548720 OntoDM_548720 data mining source code module OntoDM_549140 OntoDM_549140s OntoDM_549140 OntoDM_549140 OntoDM_549140 LCPN tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_549140 LCPN tree based HC algorithm OntoDM_555011 OntoDM_555011s OntoDM_555011 OntoDM_555011 OntoDM_555011 LCN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_555011 LCN DAG based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_559103 OntoDM_559103s OntoDM_559103 OntoDM_559103 OntoDM_559103 Tree based hierarchical classification task with single label paths is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have a single paths of labels. OntoDM_559103 tree-based hierarchical classification task with single label paths OntoDM_565974 OntoDM_565974s OntoDM_565974 OntoDM_565974 OntoDM_565974 multi-target regression dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of reals (specified with the feature set specification) on the output part of the data. OntoDM_565974 multi-target regression dataset OntoDM_570767 OntoDM_570767s OntoDM_570767 OntoDM_570767 OntoDM_570767 Multi-label classification algorithm is a data mining algorithm that solves a multi-label classification task and as a result produces a multi-label classification model. OntoDM_570767 multi-label classification algorithm OntoDM_572839 OntoDM_572839s OntoDM_572839 OntoDM_572839 OntoDM_572839 LCL tree-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_572839 LCL tree based HC algorithm for single path and mandatory leaf node prediction OntoDM_577929 OntoDM_577929s OntoDM_577929 OntoDM_577929 OntoDM_577929 A predictive model for structured output is a predictive model specification and denotes predictive models that are built on data having a structured datatype on the output part. OntoDM_577929 predictive model for structured output OntoDM_583334 OntoDM_583334s OntoDM_583334 OntoDM_583334 OntoDM_583334 Multi-target regression model is a multi-target prediction model and denotes predictive models that are built on data having a tuple of reals (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_583334 multi-target regression model OntoDM_585235 OntoDM_585235s OntoDM_585235 OntoDM_585235 OntoDM_585235 LCL DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_585235 LCL DAG based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_592921 OntoDM_592921 OntoDM_592921 HMC-ENS-Bagging algorithm OntoDM_600958 OntoDM_600958s OntoDM_600958 OntoDM_600958 OntoDM_600958 predicting structured output task is a predictive modeling task where the objective is to predict a structured output (represented with a structured datatype). OntoDM_600958 supervised structured output prediction task OntoDM_603253 OntoDM_603253s OntoDM_603253 OntoDM_603253 OntoDM_603253 scenario OntoDM_608558 OntoDM_608558s OntoDM_608558 OntoDM_608558 OntoDM_608558 unlabeled feature-based dataset is a dataset specification of unlabeled dataset with tuple of primitives as a descriptive datatype specification and descriptive feature set specification. OntoDM_608558 feature-based unlabeled dataset OntoDM_609184 OntoDM_609184s OntoDM_609184 OntoDM_609184 OntoDM_609184 binary classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a boolean datatype on the output part of the data (specified with the feature specification). OntoDM_609184 binary classification dataset OntoDM_611937 OntoDM_611937s OntoDM_611937 OntoDM_611937 OntoDM_611937 GC tree based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_613600 OntoDM_613600s OntoDM_613600 OntoDM_613600 OntoDM_613600 clustering evaluation OntoDM_613675 OntoDM_613675s OntoDM_613675 OntoDM_613675 OntoDM_613675 LCPN DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_613675 LCPN DAG based HC algorithm for single path prediction OntoDM_613950 OntoDM_613950s OntoDM_613950 OntoDM_613950 OntoDM_613950 language of graphical models OntoDM_616457 OntoDM_616457s OntoDM_616457 OntoDM_616457 OntoDM_616457 language of decision rules OntoDM_616614 OntoDM_616614s OntoDM_616614 OntoDM_616614 OntoDM_616614 software toolkit OntoDM_620805 OntoDM_620805s OntoDM_620805 OntoDM_620805 OntoDM_620805 DAG based hierarchical classification task with single label paths and full depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and all instances have full depth of labeling. OntoDM_620805 DAG based hierarchical classification task with single label paths and full depth labeling OntoDM_622093 OntoDM_622093s OntoDM_622093 OntoDM_622093 OntoDM_622093 LCL tree-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_622093 LCL tree based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_622428 OntoDM_622428s OntoDM_622428 OntoDM_622428 OntoDM_622428 Tree based hierarchical classification task with single label paths and full depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and all instances have full depth of labeling. OntoDM_622428 tree-based hierarchical classification task with single label paths and full depth labeling OntoDM_625176 OntoDM_625176s OntoDM_625176 OntoDM_625176 OntoDM_625176 evaluation algorithm implementation OntoDM_627978 OntoDM_627978s OntoDM_627978 OntoDM_627978 OntoDM_627978 Multi-target binary decision tree is a multi-target binary classification model and denotes predictive models that are built on data having a tuple of boolean (set of output features) on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and is expressed in the language of decision trees. OntoDM_627978 multi-target binary decision tree OntoDM_629944 OntoDM_629944s OntoDM_629944 OntoDM_629944 OntoDM_629944 labeled dataset with structured output is a dataset specification of a labeled dataset with structured output datatype specification. OntoDM_629944 completely labeled dataset with structured output OntoDM_636213 OntoDM_636213s OntoDM_636213 OntoDM_636213 OntoDM_636213 multi-target multi-class classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of discrete (specified with the feature set specification) on the output part of the data. OntoDM_636213 multi-target multi-class classification dataset OntoDM_636747 OntoDM_636747s OntoDM_636747 OntoDM_636747 OntoDM_636747 multi-class classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part, and a discrete datatype on the output part of the data (specified with the feature specification). OntoDM_636747 multi-class classification dataset OntoDM_640624 OntoDM_640624s OntoDM_640624 OntoDM_640624 OntoDM_640624 Hierarchical classification decision tree is a hierarchical classification model and denotes predictive models that are built on data having a labeled graph datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees. OntoDM_640624 hierarchical classification decision tree OntoDM_642324 OntoDM_642324s OntoDM_642324 OntoDM_642324 OntoDM_642324 language of decision trees OntoDM_651414 OntoDM_651414s OntoDM_651414 OntoDM_651414 OntoDM_651414 a predicted dataset is a dataset that is an output of a predictive model execution process. OntoDM_651414 predicted dataset OntoDM_661861 OntoDM_661861s OntoDM_661861 OntoDM_661861 OntoDM_661861 structure based structured output prediction task is a predicting structured output task where the objective is to predict a structured output (represented with a outputstructured datatype) from a structure (represented by a descriptive structured datatype). OntoDM_661861 supervised structure based structured output prediction task OntoDM_666475 OntoDM_666475s OntoDM_666475 OntoDM_666475 OntoDM_666475 labeled feature-based dataset with structured output is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a structured datatype on the output part of the data. OntoDM_666475 feature-based completely labeled dataset with structured output OntoDM_669483 OntoDM_669483s OntoDM_669483 OntoDM_669483 OntoDM_669483 LCL tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_669483 LCL tree based HC algorithm for single path prediction OntoDM_674255 OntoDM_674255s OntoDM_674255 OntoDM_674255 OntoDM_674255 Multi-target regression algorithm is a data mining algorithm that solves a multi-target regression task and as a result produces a multi-target regression model. OntoDM_674255 multi-target regression algorithm OntoDM_675141 OntoDM_675141s OntoDM_675141 OntoDM_675141 OntoDM_675141 descriptive set of discrete is a descriptive structured datatype specification that denotes a set of discrete datatype on the descriptive part of the data. OntoDM_675141 descriptive:set of discrete OntoDM_686944 OntoDM_686944s OntoDM_686944 OntoDM_686944 OntoDM_686944 regression dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a real datatype on the output part of the data (specified with the output specification). OntoDM_686944 regression dataset OntoDM_688833 OntoDM_688833s OntoDM_688833 OntoDM_688833 OntoDM_688833 A regression model is a feature-based predictive model for primitive output and denotes predictive models that are built on data having a real datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_688833 regression model OntoDM_689658 OntoDM_689658s OntoDM_689658 OntoDM_689658 OntoDM_689658 Multi-target multi-class decision tree is a multi-target multi-class classification model and denotes predictive models that are built on data having a tuple of discrete (set of output features) on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and are expressed in the language of decision trees. OntoDM_689658 multi-target multi-class decison tree OntoDM_692551 OntoDM_692551s OntoDM_692551 OntoDM_692551 OntoDM_692551 LCL DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_692551 LCL DAG based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_692808 OntoDM_692808s OntoDM_692808 OntoDM_692808 OntoDM_692808 labeled dataset with structured output is a dataset specification of a labeled dataset with structured datatype on the descriptive part and a structured datatype on the output part of the data. OntoDM_692808 structure-based completely labeled dataset with structured output OntoDM_693227 OntoDM_693227s OntoDM_693227 OntoDM_693227 OntoDM_693227 description OntoDM_694510 OntoDM_694510s OntoDM_694510 OntoDM_694510 OntoDM_694510 GC tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach. OntoDM_694510 GC tree-based HC algorithm for multiple path prediction OntoDM_708865 OntoDM_708865s OntoDM_708865 OntoDM_708865 OntoDM_708865 representational medium specification OntoDM_716122 OntoDM_716122s OntoDM_716122 OntoDM_716122 OntoDM_716122 output boolean specification is an output primitive datatype specification that denotes a boolean datatype on the output part of the data. OntoDM_716122 output:boolean OntoDM_718392 OntoDM_718392s OntoDM_718392 OntoDM_718392 OntoDM_718392 DAG based hierarchical classification task with multiple label paths and partial depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and at least one instance has a partial depth of labeling. OntoDM_718392 DAG based hierarchical classification task with multiple label paths and partial depth labeling OntoDM_718446 OntoDM_718446s OntoDM_718446 OntoDM_718446 OntoDM_718446 Structure-based predictive modeling algorithm for primitive output is a data mining algorithm that solves a structure-based primitive output prediction task and as a result produces a structure-based predictive model for primitive output. OntoDM_718446 structure-based prectictive modeling algorithm for primitive output OntoDM_721008 OntoDM_721008s OntoDM_721008 OntoDM_721008 OntoDM_721008 Random Forest OntoDM_722523 OntoDM_722523s OntoDM_722523 OntoDM_722523 OntoDM_722523 Multi-target classification algorithm is a data mining algorithm that solves a multi-target classification task and as a result produces a multi-target classification model. OntoDM_722523 multi-target classification algorithm OntoDM_726490 OntoDM_726490s OntoDM_726490 OntoDM_726490 OntoDM_726490 Mapping specification is a information content entity that denotes a part of a data example on which a concrete datatype applies to. OntoDM_726490 mapping specification OntoDM_727807 OntoDM_727807s OntoDM_727807 OntoDM_727807 OntoDM_727807 multi-target binary classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of booleans (specified with the feature set specification) on the output part of the data. OntoDM_727807 multi-target binary classification dataset OntoDM_728533 OntoDM_728533s OntoDM_728533 OntoDM_728533 OntoDM_728533 Multi-target binary classification algorithm is a data mining algorithm that solves a multi-target binary classification task and as a result produces a multi-target binarty classification model. OntoDM_728533 multi-target binary classification algorithm OntoDM_730046 OntoDM_730046s OntoDM_730046 OntoDM_730046 OntoDM_730046 LCN tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_730046 LCN tree based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_730934 OntoDM_730934s OntoDM_730934 OntoDM_730934 OntoDM_730934 evaluation algorithm OntoDM_731692 OntoDM_731692s OntoDM_731692 OntoDM_731692 OntoDM_731692 A single generalization algorithm is a data mining algorithm that solves a data mining task and as a result produces a single generalization. OntoDM_731692 single generalization algorithm OntoDM_733255 OntoDM_733255s OntoDM_733255 OntoDM_733255 OntoDM_733255 Multi-target classification task is a multi-target prediction task where the objective is to predict a set of binary/discrete features (represented with a output tuple of booleans or discrete) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_733255 supervised multi-target classification task OntoDM_741560 OntoDM_741560 OntoDM_741560 OntoDM_741560s OntoDM_741560 OntoDM_741560ed OntoDM_741560 is_representation_of OntoDM_741571 OntoDM_741571s OntoDM_741571 OntoDM_741571 OntoDM_741571 output primitive is an output datatype specification that denotes a primitive datatype on the output part of the data. OntoDM_741571 primitive-based output data specification OntoDM_746568 OntoDM_746568s OntoDM_746568 OntoDM_746568 OntoDM_746568 multi-target prediction dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of primitives (specified with the feature set specification) on the output part of the data. OntoDM_746568 multi-target prediction dataset OntoDM_748173 OntoDM_748173s OntoDM_748173 OntoDM_748173 OntoDM_748173 operator OntoDM_754841 OntoDM_754841s OntoDM_754841 OntoDM_754841 OntoDM_754841 GC tree based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_757135 OntoDM_757135s OntoDM_757135 OntoDM_757135 OntoDM_757135 information processing objective is objective specification of the end point that an information processing process needs to achieve at its end. OntoDM_757135 information processing objective OntoDM_757640 OntoDM_757640s OntoDM_757640 OntoDM_757640 OntoDM_757640 Multi-target multi-class classification algorithm is a data mining algorithm that solves a multi-target multi-class classification task and as a result produces a multi-target multi-class classification model. OntoDM_757640 multi-target multi-class classification algorithm OntoDM_771971 OntoDM_771971s OntoDM_771971 OntoDM_771971 OntoDM_771971 GC tree based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_776589 OntoDM_776589s OntoDM_776589 OntoDM_776589 OntoDM_776589 OntoDM_776589 OntoDM_776589 pattern discovery evaluation OntoDM_782155 OntoDM_782155s OntoDM_782155 OntoDM_782155 OntoDM_782155 LCN DAG-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_782155 LCN DAG based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_783954 OntoDM_783954s OntoDM_783954 OntoDM_783954 OntoDM_783954 multi-target classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of booleans or discrete (specified with the feature set specification) on the output part of the data. OntoDM_783954 multi-target classification dataset OntoDM_785336 OntoDM_785336s OntoDM_785336 OntoDM_785336 OntoDM_785336 Structure-based primitive output prediction task is a predicting primitive output task where the objective is to predict a primitive output (represented with a primitive datatype) from a structure (represented by a descriptive structured datatype). OntoDM_785336 supervised structure based primitive output prediction task OntoDM_793930 OntoDM_793930s OntoDM_793930 OntoDM_793930 OntoDM_793930 LCL tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_793930 LCL tree based HC algorithm OntoDM_794067 OntoDM_794067s OntoDM_794067 OntoDM_794067 OntoDM_794067 feature-based time series predicton dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a sequence of reals datatype on the output part of the data. OntoDM_794067 feature-based time-series prediction dataset OntoDM_800750 OntoDM_800750 OntoDM_800750 output specification OntoDM_804811 OntoDM_804811 OntoDM_804811 integer datatype OntoDM_813772 OntoDM_813772s OntoDM_813772 OntoDM_813772 OntoDM_813772 language of bayesian nets OntoDM_815320 OntoDM_815320s OntoDM_815320 OntoDM_815320 OntoDM_815320 Multi-target multi-class classification task is a multi-target classification task where the objective is to predict a set of discrete features (represented with a output tuple of discrete ) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_815320 supervised multi-target multi-class classification task OntoDM_817028 OntoDM_817028s OntoDM_817028 OntoDM_817028 OntoDM_817028 Multi-target predictive modeling algorithm is a data mining algorithm that solves a multi-target prediction task and as a result produces a multi-target predictive model. OntoDM_817028 multi-target predictive modelling algorithm OntoDM_817888 OntoDM_817888s OntoDM_817888 OntoDM_817888 OntoDM_817888 data mining workflow OntoDM_818411 OntoDM_818411s OntoDM_818411 OntoDM_818411 OntoDM_818411 GC DAG based HC algorithm for single path and mandatory leaf node prediction OntoDM_826903 OntoDM_826903s OntoDM_826903 OntoDM_826903 OntoDM_826903 Tree based hierarchical classification task with multiple label paths is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels. OntoDM_826903 tree-based hierarchical classification task with multiple label paths OntoDM_829270 OntoDM_829270s OntoDM_829270 OntoDM_829270 OntoDM_829270 LCPN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_829270 LCPN DAG based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_831808 OntoDM_831808s OntoDM_831808 OntoDM_831808 OntoDM_831808 Feature-based predictive modeling algorithm for structured output is a data mining algorithm that solves a feature-based primitive structured prediction task and as a result produces a feature-based predictive model for structured output. OntoDM_831808 feature-based predictive modeling algorithm for structured output OntoDM_835948 OntoDM_835948s OntoDM_835948 OntoDM_835948 OntoDM_835948 output structured specification is an output datatype specification that denotes a structured datatype on the output part of the data. OntoDM_835948 structure-based output data specification OntoDM_837886 OntoDM_837886s OntoDM_837886 OntoDM_837886 OntoDM_837886 regression classification task is a feature-based primitive output task where the objective is to predict a real output (represented with a real datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_837886 supervised regression task OntoDM_838789 OntoDM_838789s OntoDM_838789 OntoDM_838789 OntoDM_838789 LCL DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_838789 LCL DAG based HC algorithm for single path prediction OntoDM_843203 OntoDM_843203s OntoDM_843203 OntoDM_843203 OntoDM_843203 labeled structure-based dataset with primitive output is a dataset specification of a labeled dataset with a structure datatype on the descriptive part and a primitive datatype on the output part of the data (specified with the output feature specification). OntoDM_843203 structure-based completely labeled dataset with primitive output OntoDM_843647 OntoDM_843647s OntoDM_843647 OntoDM_843647 OntoDM_843647 output sequence of real specification is an output sequence datatype specification that denotes a sequence of reals datatype on the output part of the data. OntoDM_843647 output:sequence of reals OntoDM_849533 OntoDM_849533s OntoDM_849533 OntoDM_849533 OntoDM_849533 labeled dataset with primitive output is a dataset specification of labeled dataset with primitive output datatype specification. OntoDM_849533 completely labeled dataset with primitive output OntoDM_849657 OntoDM_849657s OntoDM_849657 OntoDM_849657 OntoDM_849657 GC DAG based HC algorithm for single path and non-mandatory leaf node prediction OntoDM_860244 OntoDM_860244s OntoDM_860244 OntoDM_860244 OntoDM_860244 OntoDM_860244 dataset quality OntoDM_860584 OntoDM_860584s OntoDM_860584 OntoDM_860584 OntoDM_860584 Pattern discovery ensemble algorithm is a data mining algorithm that solves a pattern discovery task and as a result produces a ensemble of set of patterns. OntoDM_860584 pattern discovery ensemble algorithm OntoDM_865023 OntoDM_865023s OntoDM_865023 OntoDM_865023 OntoDM_865023 A multi-class classification model is a classification model and denotes predictive models that are built on data having a discrete datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part. OntoDM_865023 multi-class classification model OntoDM_871327 OntoDM_871327s OntoDM_871327 OntoDM_871327 OntoDM_871327 Predictive modeling algorithm for structured output is a data mining algorithm that solves a predicting structured output task and as a result produces a predictive model for structured output. OntoDM_871327 predictive modeling algorithm for structured output OntoDM_874928 OntoDM_874928s OntoDM_874928 OntoDM_874928 OntoDM_874928 LCN DAG-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_874928 LCN DAG based HC algorithm for single path and mandatory leaf node prediction OntoDM_879928 OntoDM_879928s OntoDM_879928 OntoDM_879928 OntoDM_879928 unlabeled dataset is a dataset specification for datasets that have only descriptive datatype specification of the data examples contained in the dataset. OntoDM_879928 unlabeled dataset OntoDM_882801 OntoDM_882801s OntoDM_882801 OntoDM_882801 OntoDM_882801 LCPN DAG-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_882801 LCPN DAG based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_892395 OntoDM_892395s OntoDM_892395 OntoDM_892395 OntoDM_892395 A data example is a data item that represents one unit of data and it is a part of a dataset. It is a synonim with a case or example or observation in statistics. OntoDM_892395 data example OntoDM_895197 OntoDM_895197s OntoDM_895197 OntoDM_895197 OntoDM_895197 output set of discrete specification is an output set specification that denotes a set of discrete datatype on the output part of the data. OntoDM_895197 output:set of discrete OntoDM_900172 OntoDM_900172s OntoDM_900172 OntoDM_900172 OntoDM_900172 GC DAG based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_901970 OntoDM_901970s OntoDM_901970 OntoDM_901970 OntoDM_901970 LCN tree-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach. OntoDM_901970 LCN tree based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_908131 OntoDM_908131s OntoDM_908131 OntoDM_908131 OntoDM_908131 output labeled graph specification is an output structured datatype specification that denotes a labeled graph datatype on the output part of the data. OntoDM_908131 labeled graph output specification OntoDM_910828 OntoDM_910828s OntoDM_910828 OntoDM_910828 OntoDM_910828 Boosting OntoDM_913132 OntoDM_913132s OntoDM_913132 OntoDM_913132 OntoDM_913132 multi class classification task is a flat classification task where the objective is to predict a discrete output (represented with a boolean or discrete datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_913132 supervised multi-class classification task OntoDM_917475 OntoDM_917475s OntoDM_917475 OntoDM_917475 OntoDM_917475 evaluation algorithm execution OntoDM_918795 OntoDM_918795s OntoDM_918795 OntoDM_918795 OntoDM_918795 probability distribution evaliuation OntoDM_919659 OntoDM_919659s OntoDM_919659 OntoDM_919659 OntoDM_919659 LCPN tree-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach. OntoDM_919659 LPCN tree based HC algorithm for multiple path and non-mandatory leaf node prediction OntoDM_930862 OntoDM_930862s OntoDM_930862 OntoDM_930862 OntoDM_930862 Tree based hierarchical classification task with multiple label paths and partial depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and at least one instance has a partial depth of labeling. OntoDM_930862 tree-based hierarchical classification task with multiple label paths and partial depth labeling OntoDM_931732 OntoDM_931732s OntoDM_931732 OntoDM_931732 OntoDM_931732 Hierarchical classification task is a feature-based structured output prediction task where the objective is to predict a hierarchical output (represented with a output labeled graph datatype) from a set of features (represented by a descriptive tuple of primitives datatype). OntoDM_931732 supervised hierarchical classification task OntoDM_936293 OntoDM_936293s OntoDM_936293 OntoDM_936293 OntoDM_936293 output tuple of real specification is an output tuple of primitives specification that denotes a tuple of reals datatype on the output part of the data. OntoDM_936293 output:tuple of real OntoDM_938471 OntoDM_938471s OntoDM_938471 OntoDM_938471 OntoDM_938471 a data example specification is a data item specification entity that is about a data example. OntoDM_938471 data example specification OntoDM_941659 OntoDM_941659s OntoDM_941659 OntoDM_941659 OntoDM_941659 constraint based data mining task OntoDM_941793 OntoDM_941793s OntoDM_941793 OntoDM_941793 OntoDM_941793 Feature-based predictive modeling algorithm for primitive output is a data mining algorithm that solves a feature-based primitive output prediction task and as a result produces a feature-based predictive model for primitive output. OntoDM_941793 feature-based predictive modeling algorithm for primitive output OntoDM_942569 OntoDM_942569s OntoDM_942569 OntoDM_942569 OntoDM_942569 A structure-based predictive model for primitive output is a predictive model specification and denotes predictive models that are built on data having primitive datatype on the output part, and a structured datatype on the descriptive part. OntoDM_942569 structure-based predictive model for primitive output OntoDM_942737 OntoDM_942737s OntoDM_942737 OntoDM_942737 OntoDM_942737 LC DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier (LC) approach. OntoDM_942737 LC DAG based HC algorithm OntoDM_944653 OntoDM_944653s OntoDM_944653 OntoDM_944653 OntoDM_944653 data mining software OntoDM_947353 OntoDM_947353 OntoDM_947353 descriptive specification OntoDM_960431 OntoDM_960431s OntoDM_960431 OntoDM_960431 OntoDM_960431 hierarchical classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a labeled graph datatype on the output part of the data. OntoDM_960431 hierarchical classification dataset OntoDM_970232 OntoDM_970232s OntoDM_970232 OntoDM_970232 OntoDM_970232 Stacking OntoDM_975727 OntoDM_975727s OntoDM_975727 OntoDM_975727 OntoDM_975727 LCL DAG-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach. OntoDM_975727 LCL DAG based HC algorithm for multiple path and mandatory leaf node prediction OntoDM_982352 OntoDM_982352s OntoDM_982352 OntoDM_982352 OntoDM_982352 data mining workflow execution OntoDM_997152 OntoDM_997152s OntoDM_997152 OntoDM_997152 OntoDM_997152 generalization processing algorithm OntoDM_U1_059913 OntoDM_U1_059913s OntoDM_U1_059913 OntoDM_U1_059913 OntoDM_U1_059913 evaluation workflow execution OntoDM_U1_858511 OntoDM_U1_858511s OntoDM_U1_858511 OntoDM_U1_858511 OntoDM_U1_858511 evaluation workflow OntoDT_10000000000 completely labeled dataset OntoDT_10000000001 semi-labeled dataset OntoDT_10000000002 partially labeled dataset OntoDT_10000000003 supervised predictive modelling task OntoDT_10000000004 semi-supervised predictive modelling task OntoDT_10000000005 semi-supervised structured output prediction task OntoDT_10000000006 semi-supervised feature-based structured output prediction task OntoDT_10000000007 semi-supervised hierarchical classification task 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