{ "metadata": { "name": "", "signature": "sha256:8ac41a336bfed04318d370f989c4c38b0014933c2484d9d8473c7bbc8b1ec0a2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Data science portfolio\n", "###Trevor D Rhone\n", "###5/10/2015" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Portfolio Menu:\n", "\n", "- Project 1: http://trevorguru.github.io\n", "- Project 2: http://nbviewer.ipython.org/github/trevorguru/trevorguru.github.io/blob/master/Tproject2.ipynb\n", "- Project 3: http://nbviewer.ipython.org/github/trevorguru/trevorguru.github.io/blob/master/har-publish.ipynb\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Data Analytics of Human Activity Recognition\n", "###Using machine learning to predict qualitative features of human activity\n", "\n", "\n", "We study a dataset with five classes (sitting-down, standing-up, standing, walking, and sitting) of 'human activity' collected on eight hours of activities of four healthy subjects. \n", "The manner in which four subjects performed the five classes of 'human activity' were quantified by attaching accelerometers to parts of the body during the activity. The goal is to predict the manner in which the activity was performed. There is a 'outcome' variable in the training set which labels the activity. The features represent the data recorded from the accelerometers. We will build a model and make predictions using these features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sensors were placed on the waist (1), left thigh (2), right ankle (3), and right arm (4). See Ref [1] for details. The 12 features selected by \n", "through this procedure were: (1) Sensor on the Belt: discretization of the module of \n", "acceleration vector, variance of pitch, and variance of roll; (2) Sensor on the left \n", "thigh: module of acceleration vector, discreti\n", "zation, and variance of pitch; (3) Sensor \n", "on the right ankle: variance of pitch, and variance of roll; (4) Sensor on the right arm: \n", "discretization of the module of acceleration vector; From all sensors: average accele-\n", "ration and standard deviation of acceleration. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from sklearn import cross_validation\n", "from sklearn import svm\n", "from matplotlib import pyplot as plt\n", "from sklearn import preprocessing\n", "from sklearn import metrics\n", "%matplotlib inline \n", "import pandas as pd\n", "import matplotlib" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from minepy import MINE\n", "import copy\n", "from mpl_toolkits.mplot3d import Axes3D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import data set:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mydir = \"\"\n", "filename = mydir+\"dataset_har.csv\" \n", "#filename = 'test.csv'\n", "\n", "conv = lambda valstr: float(valstr.replace(',','.'))\n", "c = {3:conv, 4:conv, 5:conv}\n", "col = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]\n", "age = np.genfromtxt(filename,delimiter=\";\", skip_header=1 ,usecols=(2),dtype=int) \n", "weight = np.genfromtxt(filename,delimiter=\";\", skip_header=1 ,usecols=(4),dtype=int) \n", "data_height_bmi= np.genfromtxt(filename,delimiter=\";\", skip_header=1 ,usecols=(3,5),dtype=None, converters = c) \n", "data= np.genfromtxt(filename,delimiter=\";\", skip_header=1 ,usecols=col,dtype=int) \n", "data=data*1.0\n", "target = np.genfromtxt(filename,delimiter=\";\", skip_header=1 ,usecols=18,dtype=str) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preprocess data for use with machine learning algorithms:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rawdata = copy.copy(data)\n", "data = preprocessing.scale(data)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "def digitize(starget):\n", " \"\"\"\n", " Convert string output labels to floats. \n", " Machine learning classifer only accepts float as the output value.\n", " \"\"\"\n", " stringlabels= np.unique(starget)\n", " lenlabels = len(stringlabels)\n", " dindex = 0\n", " mydict = {}\n", " for dlabel in stringlabels:\n", " mydict[dlabel] = dindex\n", " dindex = dindex + 1\n", " print mydict \n", " dtarget = copy.copy(starget)\n", " for i in stringlabels:\n", " myindex = np.where(starget == i)[0]\n", " dtarget[myindex]=mydict.get(i)\n", " return dtarget" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output string labels are converted to floats as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dtarget = digitize(target)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'standing': 2, 'walking': 4, 'sittingdown': 1, 'standingup': 3, 'sitting': 0}\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next few steps we will create variables that we can use in our data visualization and machine learning algorithms. We divide data by 'class' (standig, walking etc.) to facilitate data visualization." ] }, { "cell_type": "code", "collapsed": false, "input": [ "stand_index= np.where(target=='standing')\n", "walk_index = np.where(target == 'walking')\n", "sitdown_index = np.where(target == 'sittingdown')\n", "standup_index = np.where(target == 'standingup')\n", "sit_index = np.where(target == 'sitting')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "height = data_height_bmi[:,0]\n", "BMI = data_height_bmi[:,1]\n", "\n", "x1 = data[:,0]\n", "y1 = data[:,1]\n", "z1 = data[:,2]\n", "x2 = data[:,3]\n", "y2 = data[:,4]\n", "z2 = data[:,5]\n", "x3 = data[:,6]\n", "y3 = data[:,7]\n", "z3 = data[:,8]\n", "x4 = data[:,9]\n", "y4 = data[:,10]\n", "z4 = data[:,11]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "x1r = rawdata[:,0]\n", "y1r = rawdata[:,1]\n", "z1r = rawdata[:,2]\n", "x2r = rawdata[:,3]\n", "y2r = rawdata[:,4]\n", "z2r = rawdata[:,5]\n", "x3r = rawdata[:,6]\n", "y3r = rawdata[:,7]\n", "z3r = rawdata[:,8]\n", "x4r = rawdata[:,9]\n", "y4r = rawdata[:,10]\n", "z4r = rawdata[:,11]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following plot, the markers are colored by the activity 'class'" ] }, { "cell_type": "code", "collapsed": false, "input": [ "position = (dtarget.astype(np.float))\n", "\n", "plt.subplot(2,2,1)\n", "plt.scatter(x1r,y1r,c=position, cmap=plt.cm.Paired, alpha=0.9)\n", "plt.xlabel('x1')\n", "plt.ylabel('y1')\n", "\n", "plt.subplot(2,2,2)\n", "plt.scatter(x4r,y4r,c=position, cmap=plt.cm.Paired,alpha=0.9)\n", "plt.xlabel('x4')\n", "plt.ylabel('y4')\n", "\n", "plt.subplot(2,2,3)\n", "plt.scatter(x2r,y2r,c=position, cmap=plt.cm.Paired,alpha=0.9)\n", "plt.xlabel('x2')\n", "plt.ylabel('y2')\n", "\n", "plt.subplot(2,2,4)\n", "plt.scatter(x3r,y3r,c=position, cmap=plt.cm.Paired,alpha=0.9)\n", "plt.xlabel('x3')\n", "plt.ylabel('y3')\n", "\n", "plt.subplots_adjust(hspace=0.35,wspace=0.45)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4QvDLrWL0czNGUmE1Mstr4KZVQ19qALVaDVNbR2i0WvB5PLh4+SAx9tfm+Oob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cSH6Zm7q5ZaT5BusWbya708PjQViyF0RzB1eRES4NcESUcbUa0rdiKERmpEqHVEgzdIk1dF08Co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\nIcx4XKhpBrsKGuBswcPxmy1oV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UJGRkaMp6fnTbZjNVRG9DLyhIGzb9++ZcbGxopFixYd\npmmaM2XKlPOHDx9edO7cuWn37t0bt3fv3ucA4Isvvni25+ZEBMJopTddlJSU+CYlJW3V7sGxfv36\nFJJEdIM8kRAIBAJBJ8gYCYFAIBB0giQSAoFAIOgESSQEAoFA0AmSSAgEAoGgEySREAgEAkEnSCIh\nEAgEgk78CwCUA2NibWyfAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "vec1 = rawdata[:,0:3]\n", "vec1_stand = vec1[stand_index,:][0]\n", "vec1_walk = vec1[walk_index,][0]\n", "vec1_sitdown = vec1[sitdown_index,][0]\n", "vec1_standup = vec1[standup_index,:][0]\n", "vec1_sit = vec1[sit_index,:][0]\n", "vec2 = rawdata[:,3:6]\n", "\n", "print np.squeeze(vec1_stand).shape\n", "print vec1_stand.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(47370, 3)\n", "(47370, 3)\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure()\n", "ax = Axes3D(fig, elev=-150, azim=110)\n", "ax.scatter(vec1_stand[:,0],vec1_stand[:,1],vec1_stand[:,2], marker='x',color='b',label='standing')\n", "ax.scatter(vec1_sit[:,0],vec1_sit[:,1],vec1_sit[:,2], marker='o',color='r',label='sitting',alpha=0.5)\n", "#ax.scatter(vec1_walk[:,0],vec1_walk[:,1],vec1_walk[:,2], marker='s',color='m',label='walking')\n", "ax.scatter(vec1_standup[:,0],vec1_standup[:,1],vec1_standup[:,2], marker='>',color='g',label='stand up',alpha=0.9)\n", "ax.set_title(\"Sitting, Standing, Walking\")\n", "ax.set_xlabel(\"x-axis\")\n", "#ax.w_xaxis.set_ticklabels([])\n", "ax.set_ylabel(\"y-axis\")\n", "#ax.w_yaxis.set_ticklabels([])\n", "ax.set_zlabel(\"z-axis\")\n", "#ax.w_zaxis.set_ticklabels([])\n", "\n", "plt.legend(loc='upper left', numpoints=1, ncol=3, fontsize=8, bbox_to_anchor=(0, 0))\n", "\n", "colors = ['b','r','g']\n", "markers = ['x','o','>']\n", "scatter1_proxy = matplotlib.lines.Line2D([0],[0], linestyle=\"none\", c=colors[0], marker = markers[0])\n", "scatter2_proxy = matplotlib.lines.Line2D([0],[0], linestyle=\"none\", c=colors[1], marker = markers[1])\n", "scatter3_proxy = matplotlib.lines.Line2D([0],[0], linestyle=\"none\", c=colors[2], marker = markers[2])\n", "ax.legend([scatter1_proxy, scatter2_proxy,scatter3_proxy], ['standing', 'sitting','standup'], numpoints = 1)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/axes.py:4486: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labeled objects found. \"\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QSqV4/fXXEz2kqPHVV1+hqKgI2dnZ2LZtGzZv3pxyps544HA48NhjjyE7Oxt5eXm4cuUK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\n37brfsLXX3/NvHnz0Gq1fPPNN4waNYoNGzagVqvlB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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "position = (dtarget.astype(np.float))\n", "\n", "plt.subplot(2,2,1)\n", "plt.scatter(vec1[:,0],vec1[:,1],c=position, cmap=plt.cm.Paired, alpha=0.5)\n", "plt.xlabel('x1')\n", "plt.ylabel('y1')\n", "\n", "plt.subplot(2,2,2)\n", "plt.scatter(vec1_stand[:,0],vec1_stand[:,1],alpha=0.5)\n", "plt.xlabel('x1_stand')\n", "plt.ylabel('y1_stand')\n", "\n", "plt.subplot(2,2,3)\n", "plt.scatter(vec1_sit[:,0],vec1_sit[:,1],alpha=0.5)\n", "plt.xlabel('x1_sit')\n", "plt.ylabel('y1_sot')\n", "\n", "plt.subplot(2,2,4)\n", "plt.scatter(vec1_walk[:,0],vec1_walk[:,1],alpha=0.5)\n", "plt.xlabel('x1_walk')\n", "plt.ylabel('y1_walk')\n", "\n", "plt.subplots_adjust(hspace=0.35,wspace=0.45)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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79zzUaoEJE+6jffv2CIJAZGQkc+c+zvz535CdrUajGYyr6ykMhlS6dn2Z1NQV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\nefTRW7W6hp8PaWlpHaYPXGxsLDNn3kFZW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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the cross_validation module we radomly assign training and test data from the dataset. There is a 60-40 split. Then we build a support vector machine model, make a prediction and calculate the accuracy of our prediction using a k-fold cross validation (k=5). We repeat an accuracy estimation using the metrics module - which just compares the actual results to the predicted results. Note how the evaluation metrics change depending on the method used to assess the performance of the model. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "X_train, X_test, y_train, y_test = cross_validation.train_test_split(data, dtarget, test_size=0.4, random_state=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = svm.SVC(kernel='rbf',C=10)\n", "clf.fit(X_train, y_train)\n", "prediction = (clf.predict(X_test))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "cvscore = cross_validation.cross_val_score(clf, X_test, y_test, scoring='accuracy',cv=5)\n", "print \"Accuracy is: \", np.mean(cvscore)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Accuracy is: 0.98264261516\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "my_accuracy = metrics.accuracy_score(y_test,prediction)\n", "print my_accuracy" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.98516315996\n" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The confusion matrix is quite useful for comparing the actual activity to the prediction made by the model as a function of activity class. Rows are the actual activity and columns are the predicted activity. The confusion matrix is illustrated nicely in a plot a few lines down." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.metrics import confusion_matrix\n", "cm=confusion_matrix(y_test,prediction)\n", "print cm\n", "target_label = ('sitting','sittingdown','standing','standingup','walking')\n", "# {'standing': 2, 'walking': 4, 'sittingdown': 1, 'standingup': 3, 'sitting': 0}\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[20201 7 0 9 2]\n", " [ 7 4506 47 56 38]\n", " [ 0 9 18914 23 57]\n", " [ 29 110 99 4659 32]\n", " [ 0 81 324 53 16991]]\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(target_label))\n", " plt.xticks(tick_marks, target, rotation=45)\n", " plt.yticks(tick_marks, target)\n", " #plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "# Compute confusion matrix\n", "cm = confusion_matrix(y_test, prediction)\n", "np.set_printoptions(precision=2)\n", "print('Confusion matrix, without normalization')\n", "print(cm)\n", "plt.figure()\n", "plot_confusion_matrix(cm)\n", "\n", "# Normalize the confusion matrix by row (i.e by the number of samples\n", "# in each class)\n", "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "print('Normalized confusion matrix')\n", "print(cm_normalized)\n", "plt.figure()\n", "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Confusion matrix, without normalization\n", "[[20201 7 0 9 2]\n", " [ 7 4506 47 56 38]\n", " [ 0 9 18914 23 57]\n", " [ 29 110 99 4659 32]\n", " [ 0 81 324 53 16991]]\n", "Normalized confusion matrix" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[[ 1. 0. 0. 0. 0. ]\n", " [ 0. 0.97 0.01 0.01 0.01]\n", " [ 0. 0. 1. 0. 0. ]\n", " [ 0.01 0.02 0.02 0.95 0.01]\n", " [ 0. 0. 0.02 0. 0.97]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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MmfLnu9bVr1+/19RnVVVVqUQiYbRd56e9ZC2rJcERQv+/yU7O/7Mszjc7tvVMxTjq4O2K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\n9vY+JxaLWVKpVHXz5s07IiI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R0SE9FOEAACPTQL4bPlh6PWZZX1+vjOM45uLikhQaGhpIjCYvXrz4WfdT8gAA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\niB2M5H894e/uKCkp2UV0d+R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"text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "I initially used a support vector machine with a linear kernel to analyze the above data, but this yielded a rather poor accuracy. The radial basis function kernel used above was more successful. It is useful to explore a few models in an effort to optimize classification accuracy. The random forest classifier is a popular model and will be examined below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Random Forest Classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import ensemble" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "rfc = ensemble.RandomForestClassifier(n_estimators=100)\n", "rfc.fit(X_train,y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the Random Forest Classifier we make a prediction and analyze its performance using a confusion matrix and metrics.accuracy_score function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rfc_prediction = rfc.predict(X_test)\n", "rfc_cm = confusion_matrix(y_test,rfc_prediction)\n", "print target_label\n", "print rfc_cm" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('sitting', 'sittingdown', 'standing', 'standingup', 'walking')\n", "[[20204 7 0 8 0]\n", " [ 1 4555 10 52 36]\n", " [ 0 0 18923 5 75]\n", " [ 5 79 39 4772 34]\n", " [ 0 15 36 14 17384]]\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "rf_accuracy = metrics.accuracy_score(y_test,rfc_prediction)\n", "print rf_accuracy" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.993721133818\n" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Remarks:\n", "We have succesfully classifed human activity data from sensors placed on the human body using machine learning techniques." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####References:\n", "\n", "The data used in this sample maybe be found at: http://groupware.les.inf.puc-rio.br/har and was inspired by Ref. [1]\n", "\n", "[1] Ugulino, W.; Cardador, D.; Vega, K.; Velloso, E.; Milidiu, R.; Fuks, H. Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements. Proceedings of 21st Brazilian Symposium on Artificial Intelligence. Advances in Artificial Intelligence - SBIA 2012. In: Lecture Notes in Computer Science. , pp. 52-61. Curitiba, PR: Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-34458-9. DOI: 10.1007/978-3-642-34459-6_6. " ] } ], "metadata": {} } ] }