Data Mining and Knowledge Discovery
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.
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+)
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.
precentage train-test split evaluation algorithm
Annotation property imported from OBI to denote terms that have been imported from other ontologies
a planned process of capturing information in an enduring form with the intent to communicate this information
Is a material entity that is created or changed during material processing.
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.
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.
Relation between a realizable and a process. Reciprocal relation of realizes [GOC:cjm]
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]
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
A device is a processed material which is designed to perform some function or functions
A computer is an instrument which manipulates (stores, retrieves, and processes) data according to a list of instructions.
Information processing is a process in which input information is analysed or transformed in order to produce information as output.
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.
feature-based unlabeled data stream
structure-based unlabeled data stream
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.
SubBag
transactional data stream
Set of distance functions is a collection of distance functions.
Evaluation functions measure the validity of generalizations on a given set of data.
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.
A pattern P on type T is a boolean function on objects of type T, i.e has the signature p::T-->boolean.
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.
predictive model evaluation function specification
a dataset specification is a data item specification about a dataset defined with a datatype specification of the data examples aggregated in the dataset.
dataset specification
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.
represents class of generalizations as in Dzeroski paper
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.
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.
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).
generalization interpreter specification
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.
a fold is a dataset that is an output of a cross validation sampling process.
An evaluation datum is a data item that is a specified output of generalization evaluation process.
Clustering algorithm is a data mining algorithm that solves a clustering task and as a result produces a clustering.
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.
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.
The task of estimating the joint probability distribution involves constructing a probability disctribution given a set of data of a certain datatype.
batch probability distribution estimation task
The task of pattern discovery is to find all local patterns from a given pattern language that satisfy the required conditions.
batch pattern discovery task
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.
data mining task
Scoring functions often combine evaluation functions and language cost functions.
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.
a bag is a dataset that is an output of a bootstrap sampling process
Function that is used to combine other functions
a feature set specification is a data item specification that specifies a set of features used in a specific data mining task.
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 .
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.
Background knowledge can be seen as a set of mappings that generate new features bi:T-->Ti.
feature-based data example
feature-based DM-dataset
Dataset is an aggregate of data examples.
A constant value of some property of a generalization. It is used to define constraints.
Predictive modeling algorithm is a data mining algorithm that solves a predictive modeling task and as a result produces a predictive model.
A data specifcation is-a specifcation entity that specifies on which concrete part of the data the datatype applies to.
optimization language cost function constraint
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
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.
predictive modelling algorithm execution
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
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.
Pattern discovery algorithm is a data mining algorithm that solves a pattern discovery task and as a result produces a set of patterns.
data transformation with generalization query
clustering evaluation function specification
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.
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.
Descriptive datatype specification is a data item specification that denotes the datatype of the data from the descriptive part of a dataset.
Probability distribution estimation algorithm is a data mining algorithm that solves a probability distribution estimation task and as a result produces a probability distribution.
probability distribution estimation algorithm
pattern discovery evaluation function specification
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.
batch clustering task
selection composition of generalizations query
probability distribution evaluation function specification
generalizations and data from generalizations query
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.
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.
generalizations and data from data query
probability distribution estimation algorithm execution
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.
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.
tuple output specification
tuple of booleanORdiscrete output specification
set output specification output specification
booleanORdiscrete output data specification
Regression algorithm is a data mining algorithm that solves a regression task and as a result produces a regression model.
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.
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.
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.
LPCN tree based HC algorithm for multiple path and mandatory leaf node prediction
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.
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.
DAG based hierarchical classification task with multiple label paths and full depth labeling
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.
DAG based hierarchical classification task with single label paths
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.
DAG-based hierarchical classification dataset
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.
multi-target binary classification model
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.
transactional dataset is a dataset specification of unlabeled dataset with a set of discrete datatype on the descriptive part of the data.
transactional dataset
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.
LCN tree based HC algorithm for multiple path prediction
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.
LCL DAG based HC algorithm for single path and mandatory leaf node prediction
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.
predictive models ensemble specification
data stream
data stream example
data stream representation
data stream quality
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.
LCN DAG based HC algorithm for single path prediction
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.
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.
GC DAG based HC algorithm for multiple path and mandatory leaf node prediction
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.
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).
supervised multi-target prediction task
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.
GC DAG based HC algorithm for single path prediction
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.
scenario title
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.
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.
output discrete specification is an output primitive datatype specification that denotes a discrete datatype on the output part of the data.
output set of reals specification is an output set specification that denotes a set of reals datatype on the output part of the data.
Binary classification is a data mining algorithm that solves a binary classification task and as a result produces a binary classification model.
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.
structure-based predictive model for structured output
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.
feature-based predictive model for structured output
unlabeled structure-based dataset is a dataset specification of unlabeled dataset with a structured descriptive datatype specification.
structure-based unlabeled dataset
Flat classification algorithm is a data mining algorithm that solves a classification task and as a result produces a classification model.
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.
LPCN tree based HC algorithm for multiple path prediction
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.
LCN DAG based HC algorithm for multiple path prediction
output real specification is an output primitive datatype specification that denotes a real datatype on the output part of the data.
parameter specification is a specification entity that is about a parameter.
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).
supervised feature-based primitive output prediction task
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.
feature-based predictive model for primitive output
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).
supervised time-series prediction task
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).
supervised binary classification task
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.
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.
LCPN DAG based HC algorithm for single path and mandatory leaf node prediction
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.
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).
supervised multi-target binary classification
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.
LCPN DAG based HC algorithm for multiple path prediction
predicting primitive output task is a predictive modeling task where the objective is to predict a primitive output (represented with a primitive datatype).
supervised primitive output prediction task
descriptive structured data specification is a descriptive datatype specification that denotes a structured datatype on the descriptive part of the data.
structure-based descriptive data specification
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.
tree-based hierarchical classification task with single label paths and partial depth labeling
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.
output set specification is an output structured datatype specification that denotes a set datatype on the output part of the data.
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.
LPCN tree based HC algorithm for single path and non-mandatory leaf node prediction
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).
flat classification dataset
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.
LCN tree based HC algorithm for single path and non-mandatory leaf node prediction
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.
output DAG specification is an output labeled graph specification that denotes a DAG datatype on the output part of the data.
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.
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.
DAG based hierarchical classification task with single label paths and partial depth labeling
workflow execution
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.
structure-based predictive modeling algorithm for structured output
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.
GC tree based HC algorithm for single path prediction
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.
tree-based hierarchical classification dataset
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.
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.
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.
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.
LCL DAG based HC algorithm for multiple path prediction
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.
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.
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.
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.
LCN tree based HC algorithm for single path and mandatory leaf node prediction
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).
supervised feature-based structured output prediction task
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.
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.
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.
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.
DAG based hierarchical classification task with multiple label paths
labeled dataset is a dataset specification for datasets that have both a descriptive and output specification of the data examples contained in the dataset.
labeled dataset
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.
tree-based hierarchical classification task with multiple label paths and full depth labeling
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.
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.
LPCN tree based HC algorithm for single path and mandatory leaf node prediction
descriptive tuple of primitives specification is a descriptive datatype specification that denotes a tuple of primitives datatype on the descriptive part of the data.
feature-based descriptive data specification
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.
predictive modeling algorithm for primitive output
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).
supervised flat classification task
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.
LCL tree based HC algorithm for multiple path prediction
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.
data specification
batch data mining task
online data mining task
online clustering task
online pattern discovery task
online probability distribution estimation task
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.
LCN DAG based HC algorithm for multiple path and non-mandatory leaf node prediction
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.
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.
Clustering ensemble algorithm is a data mining algorithm that solves a clustering task and as a result produces an ensemble of clusterings.
output tree specification is an output labeled graph specification that denotes a tree datatype on the output part of the data.
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.
time-series predictive modelling algorithm
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.
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.
probability distribution estimation ensemble algorithm
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.
LCPN DAG based HC algorithm for multiple path and non-mandatory leaf node prediction
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.
LPCN tree based HC algorithm for single path prediction
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.
LCL tree based HC algorithm for single path and non-mandatory leaf node prediction
workflow
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.
multi-target multi-class classification model
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.
GC DAG based HC algorithm for multiple path prediction
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.
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).
supervised DAG based hierarchical classification task
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.
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).
supervised multi-target regression task
Hierarchical classification algorithm is a data mining algorithm that solves a hierarchical classification task and as a result produces a hierarchical classification model.
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).
supervised multi-label classification task
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.
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.
LCN tree based HC algorithm for single path prediction
output sequence specification is an output structured datatype specification that denotes a sequence datatype on the output part of the data.
GC tree based HC algorithm for single path and mandatory leaf node prediction
LCL tree based HC algorithm for multiple path and non-mandatory leaf node prediction
A ensemble algorithm is a data mining algorithm that solves a data mining task and as a result produces an ensemble of generalizations.
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.
probability distributions ensemble specification
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).
feature-based completely labeled dataset with primitive output
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.
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.
multi-label classification dataset
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).
supervised tree-based hierarchical classification task
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)
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.
LCN DAG based HC algorithm for single path and non-mandatory leaf node prediction
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.
tree-based hierarchical classification task with single label paths
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.
multi-target regression dataset
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.
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.
LCL tree based HC algorithm for single path and mandatory leaf node prediction
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.
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.
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.
LCL DAG based HC algorithm for multiple path and non-mandatory leaf node prediction
predicting structured output task is a predictive modeling task where the objective is to predict a structured output (represented with a structured datatype).
supervised structured output prediction task
scenario
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.
feature-based unlabeled dataset
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).
binary classification dataset
GC tree based HC algorithm for single path and non-mandatory leaf node prediction
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.
LCPN DAG based HC algorithm for single path prediction
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.
DAG based hierarchical classification task with single label paths and full depth labeling
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.
LCL tree based HC algorithm for multiple path and mandatory leaf node prediction
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.
tree-based hierarchical classification task with single label paths and full depth labeling
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.
labeled dataset with structured output is a dataset specification of a labeled dataset with structured output datatype specification.
completely labeled dataset with structured output
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.
multi-target multi-class classification dataset
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).
multi-class classification dataset
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.
hierarchical classification decision tree
a predicted dataset is a dataset that is an output of a predictive model execution process.
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).
supervised structure based structured output prediction task
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.
feature-based completely labeled dataset with structured output
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.
LCL tree based HC algorithm for single path prediction
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.
descriptive set of discrete is a descriptive structured datatype specification that denotes a set of discrete datatype on the descriptive part of the data.
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).
regression dataset
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.
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.
LCL DAG based HC algorithm for single path and non-mandatory leaf node prediction
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.
structure-based completely labeled dataset with structured output
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.
GC tree-based HC algorithm for multiple path prediction
output boolean specification is an output primitive datatype specification that denotes a boolean datatype on the output part of the data.
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.
DAG based hierarchical classification task with multiple label paths and partial depth labeling
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.
structure-based prectictive modeling algorithm for primitive output
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.
Mapping specification is a information content entity that denotes a part of a data example on which a concrete datatype applies to.
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.
multi-target binary classification dataset
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.
multi-target binary classification algorithm
LCN tree based HC algorithm for multiple path and non-mandatory leaf node prediction
A single generalization algorithm is a data mining algorithm that solves a data mining task and as a result produces a single generalization.
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).
supervised multi-target classification task
output primitive is an output datatype specification that denotes a primitive datatype on the output part of the data.
primitive-based output data specification
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.
multi-target prediction dataset
GC tree based HC algorithm for multiple path and mandatory leaf node prediction
information processing objective is objective specification of the end point that an information processing process needs to achieve at its end.
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.
multi-target multi-class classification algorithm
GC tree based HC algorithm for multiple path and non-mandatory leaf node prediction
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.
LCN DAG based HC algorithm for multiple path and mandatory leaf node prediction
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.
multi-target classification dataset
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).
supervised structure based primitive output prediction task
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.
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.
feature-based time-series prediction dataset
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).
supervised multi-target multi-class classification task
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.
multi-target predictive modelling algorithm
data mining workflow
GC DAG based HC algorithm for single path and mandatory leaf node prediction
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.
tree-based hierarchical classification task with multiple label paths
LCPN DAG based HC algorithm for single path and non-mandatory leaf node prediction
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.
feature-based predictive modeling algorithm for structured output
output structured specification is an output datatype specification that denotes a structured datatype on the output part of the data.
structure-based output data specification
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).
supervised regression task
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.
LCL DAG based HC algorithm for single path prediction
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).
structure-based completely labeled dataset with primitive output
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.
labeled dataset with primitive output is a dataset specification of labeled dataset with primitive output datatype specification.
completely labeled dataset with primitive output
GC DAG based HC algorithm for single path and non-mandatory leaf node prediction
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.
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.
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.
predictive modeling algorithm for structured output
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.
LCN DAG based HC algorithm for single path and mandatory leaf node prediction
unlabeled dataset is a dataset specification for datasets that have only descriptive datatype specification of the data examples contained in the dataset.
unlabeled dataset
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.
LCPN DAG based HC algorithm for multiple path and mandatory leaf node prediction
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.
output set of discrete specification is an output set specification that denotes a set of discrete datatype on the output part of the data.
GC DAG based HC algorithm for multiple path and non-mandatory leaf node prediction
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.
LCN tree based HC algorithm for multiple path and mandatory leaf node prediction
output labeled graph specification is an output structured datatype specification that denotes a labeled graph datatype on the output part of the data.
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).
supervised multi-class classification task
LPCN tree based HC algorithm for multiple path and non-mandatory leaf node prediction
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.
tree-based hierarchical classification task with multiple label paths and partial depth labeling
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).
supervised hierarchical classification task
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.
a data example specification is a data item specification entity that is about a data example.
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.
feature-based predictive modeling algorithm for primitive output
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.
structure-based predictive model for primitive output
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.
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.
hierarchical classification dataset
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.
LCL DAG based HC algorithm for multiple path and mandatory leaf node prediction
data mining workflow execution
evaluation workflow execution
evaluation workflow
completely labeled dataset
semi-labeled dataset
partially labeled dataset
supervised predictive modelling task
semi-supervised predictive modelling task
semi-supervised structured output prediction task
semi-supervised feature-based structured output prediction task
semi-supervised hierarchical classification task
semi-supervised DAG based hierarchical classification task
semi-supervised tree-based hierarchical classification task
semi-supervised multi-label classification task
semi-supervised multi-target prediction task
semi-supervised multi-target classification task
semi-supervised multi-target binary classification task
semi-supervised multi-target multi-class classification task
semi-supervised multi-target regression task
semi-supervised time-series prediction task
semi-supervised structure-based structured output prediction task
semi-supervised primitive output prediction task
semi-supervised feature-based primitive output prediction task
semi-supervised flat classification task
semi-supervised binary classification task
semi-supervised multi-class classification task
semi-supervised regression task
semi-supervised structure-based primitive output prediction task
online predictive modeling task
batch predictive modeling task
online supervised predictive modeling task
online semi-supervised predictive modeling task
online semi-supervised structured output prediction task
online semi-supervised feature-based structured output prediction task
online semi-supervised hierarchical classification task
online semi-supervised DAG based hierarchical classification task
online semi-supervised tree based hierarchical classification task
online semi-supervised multi-label classification task
online semi-supervised multi-target prediction task
online semi-supervised multi-target classification task
online semi-supervised multi-target binary classification task
online semi-supervised multi-target multi-class classification task
online semi-supervised multi-target regression task
online semi-supervised time-series prediction task
online semi-supervised structure-based structured output prediction task
online semi-supervised primitive output prediction task
online semi-supervised feature-based primitive output prediction task
online semi-supervised flat classification task
online semi-supervised binary classification task
online semi-supervised multi-class classification task
online semi-supervised regression task
online semi-supervised structure-based primitive output prediction task
online supervised structured output prediction task
online supervised feature-based structured output prediction task
online supervised hierarchical classification task
online supervised DAG based hierarchical classification task
online supervised tree-based hierarchical classification task
online supervised multi-label classification task
online supervised multi-target prediction task
online supervised multi-target classification task
online supervised multi-target binary classification task
online supervised multi-target multi-class classification task
online supervised multi-target regression task
online supervised time-series prediction task
online supervised structure-based structured output prediction task
online supervised primitive output prediction task
online supervised feature-based primitive output prediction task
online supervised flat classification task
online supervised binary classification task
online supervised multi-class classification task
online supervised regression task
online supervised structure-based primitive output prediction task
data stream specification
labeled data stream
completely labeled data stream
semi-labeled data stream
partially labeled data stream
unlabeled data stream
semi-labeled dataset with structured output
feature-based semi-labeled dataset with structured output
feature-based time-series semi-labeled prediction dataset
hierarchical semi-labeled classification dataset
DAG-based hierarchical semi-labeled classification dataset
tree-based hierarchical semi-labeled classification dataset
multi-label semi-labeled classification dataset
multi-target semi-labeled prediction dataset
multi-target semi-labeled binary classification dataset
multi-target semi-labeled multi-class classification dataset
multi-target semi-labeled regression dataset
structure-based semi-labeled dataset with structured output
semi-labeled dataset with primitive output
feature-based semi-labeled dataset with primitive output
flat semi-labeled classification dataset
binary semi-labeled classification dataset
multi-class semi-labeled classification dataset
semi-labeled regression dataset
structure-based semi-labeled dataset with primitive output
semi-labeled data stream with structured output
feature-based semi-labeled data stream with structured output
feature-based time-series semi-labeled prediction data stream
hierarchical semi-labeled classification data stream
DAG-based hierarchical semi-labeled classification data stream
tree-based hierarchical semi-labeled classification data stream
multi-label semi-labeled classification data stream
multi-target semi-labeled prediction data stream
multi-target semi-labeled binary classification data stream
multi-target semi-labeled multi-class classification data stream
multi-target semi-labeled regression data stream
structure-based semi-labeled data stream with structured output
semi-labeled data stream with primitive output
feature-based semi-labeled data stream with primitive output
flat semi-labeled classification data stream
binary semi-labeled classification data stream
multi-class semi-labeled classification data stream
semi-labeled regression data stream
structure-based semi-labeled data stream with primitive output
multi-target semi-labeled classification data stream
multi-target semi-labeled classification dataset
completely labeled data stream with structured output
feature-based completely labeled data stream with structured output
feature-based time-series prediction data stream
hierarchical classification data stream
DAG-based hierarchical classification data stream
tree-based hierarchical classification data stream
multi-label classification data stream
multi-target prediction data stream
multi-target classification data stream
multi-target binary classification data stream
multi-target multi-class classification data stream
multi-target regression data stream
structure-based completely labeled data stream with structured output
completely labeled data stream with primitive output
feature-based completely labeled data stream with primitive output
flat classification data stream
binary classification data stream
multi-class classification data stream
regression data stream
structure-based completely labeled data stream with primitive output