List of usage examples for weka.classifiers.meta Bagging Bagging
public Bagging()
From source file:AaronTest.TransformEnsembleExperiments.java
public static void ensembleTest(Instances train, Instances test) { //create a bagging ensemble and give it 1-NN as a base. Bagging bag = new Bagging(); bag.setClassifier(new IBk(1)); //train our bag try {/* w w w .j av a 2s. c o m*/ bag.buildClassifier(train); } catch (Exception ex) { System.out.println("Ex: " + ex); } double average = utilities.ClassifierTools.accuracy(test, bag); System.out.println("Average correct = " + average); }
From source file:cezeri.feature.selection.FeatureSelectionInfluence.java
public static void main(String[] args) throws Exception { String filePath = "C:\\Users\\BAP1\\Google Drive\\DataSet\\Weka_Files\\dental_florisis\\kayac_dental_2.arff"; Classifier[] models = { new MultilayerPerceptron(), new Bagging(), new REPTree() }; Influence[] dFeature = getMostDiscriminativeFeature(filePath, models[2]); System.out.println("Most Disciriminative Features are"); for (int i = 0; i < dFeature.length; i++) { System.out.println(dFeature[i].attributeName + "=" + dFeature[i].infVal); }/* w w w .j a va2s . c o m*/ }
From source file:com.entopix.maui.filters.MauiFilter.java
License:Open Source License
/** * Builds the classifier./*from w w w . j a v a 2s . c o m*/ * @throws MauiFilterException */ private void buildClassifier() throws MauiFilterException { // Generate input format for classifier FastVector atts = new FastVector(); for (int i = 0; i < getInputFormat().numAttributes(); i++) { if (i == documentAtt) { atts.addElement(new Attribute("Term_frequency")); // 0 atts.addElement(new Attribute("IDF")); // 1 atts.addElement(new Attribute("TFxIDF")); // 2 atts.addElement(new Attribute("First_occurrence")); // 3 atts.addElement(new Attribute("Last_occurrence")); // 4 atts.addElement(new Attribute("Spread")); // 5 atts.addElement(new Attribute("Domain_keyphraseness")); // 6 atts.addElement(new Attribute("Length")); // 7 atts.addElement(new Attribute("Generality")); // 8 atts.addElement(new Attribute("Node_degree")); // 9 atts.addElement(new Attribute("Wikipedia_keyphraseness")); // 10 atts.addElement(new Attribute("Wikipedia_inlinks")); // 11 atts.addElement(new Attribute("Wikipedia_generality")); // 12 } else if (i == keyphrasesAtt) { if (nominalClassValue) { FastVector vals = new FastVector(2); vals.addElement("False"); vals.addElement("True"); atts.addElement(new Attribute("Keyphrase?", vals)); } else { atts.addElement(new Attribute("Keyphrase?")); } } } classifierData = new Instances("ClassifierData", atts, 0); classifierData.setClassIndex(numFeatures); if (debugMode) { log.info("--- Converting instances for classifier"); } int totalDocuments = getInputFormat().numInstances(); // Convert pending input instances into data for classifier for (int i = 0; i < totalDocuments; i++) { Instance current = getInputFormat().instance(i); // Get the key phrases for the document String keyphrases = current.stringValue(keyphrasesAtt); HashMap<String, Counter> hashKeyphrases = getGivenKeyphrases(keyphrases); // Get the phrases for the document HashMap<String, Candidate> candidateList = allCandidates.get(current); // Compute the feature values for each phrase and // add the instance to the data for the classifier int countPos = 0; int countNeg = 0; if (debugMode) { log.info("--- Computing features for document " + i + " out of " + totalDocuments + "..."); } for (Candidate candidate : candidateList.values()) { // ignore all candidates that appear less than a threshold if (candidate.getFrequency() < minOccurFrequency) { continue; } // compute feature values double[] vals = computeFeatureValues(candidate, true, hashKeyphrases, candidateList); if (vals[vals.length - 1] == 0) { countNeg++; } else { countPos++; } Instance inst = new Instance(current.weight(), vals); // log.info(candidate + "\t" + inst); classifierData.add(inst); } log.debug(countPos + " positive; " + countNeg + " negative instances"); } log.debug("--- Building classifier"); if (classifier == null) { // Build classifier if (nominalClassValue) { // FilteredClassifier fclass = new FilteredClassifier(); // fclass.setClassifier(new NaiveBayesSimple()); // fclass.setFilter(new Discretize()); // classifier = fclass; classifier = new Bagging(); // try also // try { classifier.setOptions( Utils.splitOptions("-P 10 -S 1 -I 10 -W weka.classifiers.trees.J48 -- -U -M 2")); } catch (Exception e) { log.warn("Exception while loading classifier's options " + e.getMessage()); } } else { classifier = new Bagging(); // try also // classifier.setOptions(Utils.splitOptions("-P 10 -S 1 -I 10 -W // weka.classifiers.trees.J48 -- -U -M 2")) ; try { String optionsString = "-P 100 -S 1 -I 10 -W weka.classifiers.trees.M5P -- -U -M 7.0"; String[] options = Utils.splitOptions(optionsString); classifier.setOptions(options); } catch (Exception e) { log.warn("Exception while loading classifier's options " + e.getMessage()); } } } try { classifier.buildClassifier(classifierData); } catch (Exception e) { throw new MauiFilterException("Exception while building classifier " + e.getMessage()); } if (debugMode) { log.info(classifier.toString()); } // Save space classifierData = new Instances(classifierData, 0); }
From source file:com.rokittech.ml.server.utils.MLUtils.java
License:Open Source License
public static Classifier getClassifier(String mlAlgorithm) { notEmpty(mlAlgorithm);//from ww w. j a v a2s. c o m Classifier classifier; switch (mlAlgorithm.toUpperCase()) { case "J48": { classifier = new J48(); break; } case "IBK": { classifier = new IBk(); break; } case "NAIVE_BAYES": { classifier = new NaiveBayes(); break; } case "RANDOM_TREE": { classifier = new RandomTree(); break; } case "RANDOM_FOREST": { classifier = new RandomForest(); break; } case "BOOSTING": { classifier = new DecisionStump(); break; } case "BAGGING": { classifier = new Bagging(); break; } default: throw new UnsupportedOperationException("Classifier " + mlAlgorithm + " is not supported."); } return classifier; }
From source file:com.walmart.productgenome.matching.models.EMSRandomForest.java
License:Open Source License
/** * Builds a classifier for a set of instances. * * @param data the instances to train the classifier with * @throws Exception if something goes wrong *//*from w w w .ja va 2 s.c o m*/ public void buildClassifier(Instances data) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); m_bagger = new Bagging(); RandomTree rTree = new RandomTree(); // set up the random tree options m_KValue = m_numFeatures; if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes()) + 1; rTree.setKValue(m_KValue); rTree.setMaxDepth(getMaxDepth()); // set up the bagger and build the forest m_bagger.setClassifier(rTree); m_bagger.setSeed(m_randomSeed); m_bagger.setNumIterations(m_numTrees); m_bagger.setCalcOutOfBag(true); m_bagger.setNumExecutionSlots(m_numExecutionSlots); m_bagger.buildClassifier(data); }
From source file:ensembles.RandomForests.java
public void runClassBalanced() { ClassBalancedND classBalanced = new ClassBalancedND(); Bagging f = new Bagging(); f.setClassifier(classBalanced);/*from w ww.j a v a 2s .c om*/ String[] arg = new String[2]; arg[0] = "-t"; //training arg[1] = "/Users/katie/NetBeansProjects/weka/trunk/packages/internal/ensemblesOfNestedDichotomies/out/nd/mfeatFac/NDfold1.arff"; // arg[2] = "-T"; // arg[3] = "/Users/katie/NetBeansProjects/weka/trunk/packages/internal/ensemblesOfNestedDichotomies/out/nd/mfeatFac/NDtest1.arff"; // arg[2] = "-W"; //full name of the base classifier // arg[3] = "weka.classifiers.functions.Logistic"; AbstractClassifier.runClassifier(f, arg); }
From source file:es.ubu.XRayDetector.modelo.Fachada.java
License:Open Source License
/** * Creates a model training a classifier using bagging. * /*from ww w . j a v a 2 s . c o m*/ * @param data Contains all the instances of the ARFF file * @param sizeWindow The size of the window */ public void createModel(Instances data, String sizeWindow) { // se crea, opciones, setiputformat Classifier cls = null; //String separator = System.getProperty("file.separator"); String path = prop.getPathModel(); int opcionClasificacion = prop.getTipoClasificacion(); switch (opcionClasificacion) { case 0: //CLASIFICADOR CLASES NOMINALES (TRUE,FALSE) Classifier base; base = new REPTree(); cls = new Bagging(); ((Bagging) cls).setNumIterations(25); ((Bagging) cls).setBagSizePercent(100); ((Bagging) cls).setNumExecutionSlots(Runtime.getRuntime().availableProcessors()); ((Bagging) cls).setClassifier(base); break; case 1: //REGRESIN LINEAL (CLASES NUMRICAS, 1,0) cls = new REPTree(); break; } ObjectOutputStream oos = null; try { data.setClassIndex(data.numAttributes() - 1); cls.buildClassifier(data); /*if (arffName.contains("mejores")) oos = new ObjectOutputStream(new FileOutputStream((path + separator + "Modelos" + separator + "Bagging_" + "mejores_" + sizeWindow + ".model"))); if (arffName.contains("todas"))*/ oos = new ObjectOutputStream(new FileOutputStream((path + "todas_" + sizeWindow + ".model"))); oos.writeObject(cls); oos.flush(); oos.close(); } catch (Exception e) { throw new RuntimeException(e); } }
From source file:filters.MauiFilter.java
License:Open Source License
/** * Builds the classifier./*from w w w .j a v a 2 s . c o m*/ */ private void buildClassifier() throws Exception { // Generate input format for classifier FastVector atts = new FastVector(); for (int i = 0; i < getInputFormat().numAttributes(); i++) { if (i == documentAtt) { atts.addElement(new Attribute("Term_frequency")); // 2 atts.addElement(new Attribute("IDF")); // atts.addElement(new Attribute("TFxIDF")); // atts.addElement(new Attribute("First_occurrence")); // atts.addElement(new Attribute("Last_occurrence")); // atts.addElement(new Attribute("Spread")); // atts.addElement(new Attribute("Domain_keyphraseness")); // atts.addElement(new Attribute("Length")); // atts.addElement(new Attribute("Generality")); // atts.addElement(new Attribute("Node_degree")); // atts.addElement(new Attribute("Semantic_relatedness")); // atts.addElement(new Attribute("Wikipedia_keyphraseness")); // atts.addElement(new Attribute("Inverse_Wikip_frequency")); // atts.addElement(new Attribute("Total_Wikip_keyphraseness")); // 13 } else if (i == keyphrasesAtt) { if (nominalClassValue) { FastVector vals = new FastVector(2); vals.addElement("False"); vals.addElement("True"); atts.addElement(new Attribute("Keyphrase?", vals)); } else { atts.addElement(new Attribute("Keyphrase?")); } } } classifierData = new Instances("ClassifierData", atts, 0); classifierData.setClassIndex(numFeatures); if (debugMode) { System.err.println("--- Converting instances for classifier"); } int totalDocuments = getInputFormat().numInstances(); // Convert pending input instances into data for classifier for (int i = 0; i < totalDocuments; i++) { Instance current = getInputFormat().instance(i); // Get the key phrases for the document String keyphrases = current.stringValue(keyphrasesAtt); HashMap<String, Counter> hashKeyphrases = getGivenKeyphrases(keyphrases); // Get the phrases for the document HashMap<String, Candidate> candidateList = allCandidates.get(current); // Compute the feature values for each phrase and // add the instance to the data for the classifier int countPos = 0; int countNeg = 0; if (debugMode) { System.err .println("--- Computing features for document " + i + " out of " + totalDocuments + "..."); } for (Candidate candidate : candidateList.values()) { // ignore all candidates that appear less than a threshold if (candidate.getFrequency() < minOccurFrequency) { continue; } // compute feature values double[] vals = computeFeatureValues(candidate, true, hashKeyphrases, candidateList); if (vals[vals.length - 1] == 0) { countNeg++; } else { countPos++; } Instance inst = new Instance(current.weight(), vals); // System.out.println(candidate + "\t" + inst); classifierData.add(inst); } if (debugMode) { System.err.println(countPos + " positive; " + countNeg + " negative instances"); } } if (debugMode) { System.err.println("--- Building classifier"); } if (classifier == null) { // Build classifier if (nominalClassValue) { // FilteredClassifier fclass = new FilteredClassifier(); // fclass.setClassifier(new NaiveBayesSimple()); // fclass.setFilter(new Discretize()); // classifier = fclass; classifier = new Bagging(); // try also // classifier.setOptions( Utils.splitOptions("-P 10 -S 1 -I 10 -W weka.classifiers.trees.J48 -- -U -M 2")); } else { classifier = new Bagging(); // try also // classifier.setOptions(Utils.splitOptions("-P 10 -S 1 -I 10 -W // weka.classifiers.trees.J48 -- -U -M 2")) ; String optionsString = "-P 100 -S 1 -I 10 -W weka.classifiers.trees.M5P -- -U -M 7.0"; String[] options = Utils.splitOptions(optionsString); classifier.setOptions(options); } } FileOutputStream out = new FileOutputStream(new File("19docs.arff")); PrintWriter printer = new PrintWriter(out); printer.write(classifierData.toString()); printer.close(); out.close(); classifier.buildClassifier(classifierData); if (debugMode) { System.err.println(classifier); } // Save space classifierData = new Instances(classifierData, 0); }
From source file:fr.unice.i3s.rockflows.experiments.main.IntermediateExecutor.java
private List<InfoClassifier> inputClassifier(Dataset original) throws Exception { List<InfoClassifier> cls = new ArrayList<>(); int id = 0;//from w ww . ja v a 2 s .c om //LogisticRegression: InfoClassifier ic1 = new InfoClassifier(id++); ic1.classifier = new Logistic(); ic1.name = "Logistic Regression"; ic1.properties.requireNumericDataset = true; cls.add(ic1); //SVM: InfoClassifier ic2 = new InfoClassifier(id++); LibSVM ccc = new LibSVM(); //disable ccc.setOptions(new String[] { "-J", //Turn off nominal to binary conversion. "-V" //Turn off missing value replacement }); //ccc.setSVMType(new SelectedTag(LibSVM.SVMTYPE_C_SVC, LibSVM.TAGS_SVMTYPE)); //ccc.setKernelType(new SelectedTag(LibSVM.KERNELTYPE_RBF, LibSVM.TAGS_KERNELTYPE)); //ccc.setEps(0.001); //tolerance ic2.classifier = ccc; ic2.name = "Svm"; ic2.properties.requireNumericDataset = true; cls.add(ic2); //J48: InfoClassifier ic3 = new InfoClassifier(id++); ic3.classifier = new J48(); ic3.name = "J48"; ic3.properties.manageMissingValues = true; cls.add(ic3); //NBTree: InfoClassifier ic4 = new InfoClassifier(id++); ic4.classifier = new NBTree(); ic4.name = "NBTree"; ic4.properties.manageMissingValues = true; cls.add(ic4); //RandomForest: InfoClassifier ic5 = new InfoClassifier(id++); RandomForest ccc2 = new RandomForest(); ccc2.setNumTrees(500); ccc2.setMaxDepth(0); ic5.classifier = ccc2; ic5.name = "Random Forest"; ic5.properties.manageMissingValues = true; cls.add(ic5); //Logistic Model Trees (LMT): InfoClassifier ic6 = new InfoClassifier(id++); ic6.classifier = new LMT(); ic6.name = "Logistic Model Tree"; ic6.properties.manageMissingValues = true; cls.add(ic6); //Alternating Decision Trees (ADTree): InfoClassifier ic7 = new InfoClassifier(id++); if (original.trainingSet.numClasses() > 2) { MultiClassClassifier mc = new MultiClassClassifier(); mc.setOptions(new String[] { "-M", "3" }); //1 vs 1 mc.setClassifier(new ADTree()); ic7.classifier = mc; ic7.name = "1-vs-1 Alternating Decision Tree"; } else { ic7.classifier = new ADTree(); ic7.name = "Alternating Decision Tree"; } ic7.properties.manageMultiClass = false; ic7.properties.manageMissingValues = true; cls.add(ic7); //Naive Bayes: InfoClassifier ic8 = new InfoClassifier(id++); ic8.classifier = new NaiveBayes(); ic8.name = "Naive Bayes"; ic8.properties.manageMissingValues = true; cls.add(ic8); //Bayesian Networks: /* All Bayes network algorithms implemented in Weka assume the following for the data set: all variables are discrete finite variables. If you have a data set with continuous variables, you can use the following filter to discretize them: weka.filters.unsupervised.attribute.Discretize no instances have missing values. If there are missing values in the data set, values are filled in using the following filter: weka.filters.unsupervised.attribute.ReplaceMissingValues The first step performed by buildClassifier is checking if the data set fulfills those assumptions. If those assumptions are not met, the data set is automatically filtered and a warning is written to STDERR.2 */ InfoClassifier ic9 = new InfoClassifier(id++); ic9.classifier = new BayesNet(); ic9.name = "Bayesian Network"; ic9.properties.requireNominalDataset = true; cls.add(ic9); //IBK InfoClassifier ic10 = new InfoClassifier(id++); ic10.classifier = new IBk(); ic10.name = "IBk"; ic10.properties.manageMissingValues = true; cls.add(ic10); //JRip: InfoClassifier ic11 = new InfoClassifier(id++); ic11.classifier = new JRip(); ic11.name = "JRip"; ic11.properties.manageMissingValues = true; cls.add(ic11); //MultilayerPerceptron(MLP): InfoClassifier ic12 = new InfoClassifier(id++); ic12.classifier = new MultilayerPerceptron(); ic12.name = "Multillayer Perceptron"; ic12.properties.requireNumericDataset = true; cls.add(ic12); //Bagging RepTree: InfoClassifier ic14 = new InfoClassifier(id++); REPTree base3 = new REPTree(); Bagging ccc4 = new Bagging(); ccc4.setClassifier(base3); ic14.classifier = ccc4; ic14.name = "Bagging RepTree"; ic14.properties.manageMissingValues = true; cls.add(ic14); //Bagging J48 InfoClassifier ic15 = new InfoClassifier(id++); Bagging ccc5 = new Bagging(); ccc5.setClassifier(new J48()); ic15.classifier = ccc5; ic15.name = "Bagging J48"; ic15.properties.manageMissingValues = true; cls.add(ic15); //Bagging NBTree InfoClassifier ic16 = new InfoClassifier(id++); Bagging ccc6 = new Bagging(); ccc6.setClassifier(new NBTree()); ic16.classifier = ccc6; ic16.name = "Bagging NBTree"; ic16.properties.manageMissingValues = true; cls.add(ic16); //Bagging OneR: InfoClassifier ic17 = new InfoClassifier(id++); Bagging ccc7 = new Bagging(); ccc7.setClassifier(new OneR()); ic17.classifier = ccc7; ic17.name = "Bagging OneR"; ic17.properties.requireNominalDataset = true; ic17.properties.manageMissingValues = true; cls.add(ic17); //Bagging Jrip InfoClassifier ic18 = new InfoClassifier(id++); Bagging ccc8 = new Bagging(); ccc8.setClassifier(new JRip()); ic18.classifier = ccc8; ic18.name = "Bagging JRip"; ic18.properties.manageMissingValues = true; cls.add(ic18); //MultiboostAB DecisionStump InfoClassifier ic24 = new InfoClassifier(id++); MultiBoostAB ccc14 = new MultiBoostAB(); ccc14.setClassifier(new DecisionStump()); ic24.classifier = ccc14; ic24.name = "MultiboostAB DecisionStump"; ic24.properties.manageMissingValues = true; cls.add(ic24); //MultiboostAB OneR InfoClassifier ic25 = new InfoClassifier(id++); MultiBoostAB ccc15 = new MultiBoostAB(); ccc15.setClassifier(new OneR()); ic25.classifier = ccc15; ic25.name = "MultiboostAB OneR"; ic25.properties.requireNominalDataset = true; cls.add(ic25); //MultiboostAB J48 InfoClassifier ic27 = new InfoClassifier(id++); MultiBoostAB ccc17 = new MultiBoostAB(); ccc17.setClassifier(new J48()); ic27.classifier = ccc17; ic27.name = "MultiboostAB J48"; ic27.properties.manageMissingValues = true; cls.add(ic27); //MultiboostAB Jrip InfoClassifier ic28 = new InfoClassifier(id++); MultiBoostAB ccc18 = new MultiBoostAB(); ccc18.setClassifier(new JRip()); ic28.classifier = ccc18; ic28.name = "MultiboostAB JRip"; cls.add(ic28); //MultiboostAB NBTree InfoClassifier ic29 = new InfoClassifier(id++); MultiBoostAB ccc19 = new MultiBoostAB(); ccc19.setClassifier(new NBTree()); ic29.classifier = ccc19; ic29.name = "MultiboostAB NBTree"; ic29.properties.manageMissingValues = true; cls.add(ic29); //RotationForest RandomTree InfoClassifier ic32 = new InfoClassifier(id++); RotationForest ccc21 = new RotationForest(); RandomTree rtr5 = new RandomTree(); rtr5.setMinNum(2); rtr5.setAllowUnclassifiedInstances(true); ccc21.setClassifier(rtr5); ic32.classifier = ccc21; ic32.name = "RotationForest RandomTree"; ic32.properties.manageMissingValues = true; cls.add(ic32); //RotationForest J48: InfoClassifier ic33 = new InfoClassifier(id++); J48 base6 = new J48(); RotationForest ccc22 = new RotationForest(); ccc22.setClassifier(base6); ic33.classifier = ccc22; ic33.name = "RotationForest J48"; ic33.properties.manageMissingValues = true; cls.add(ic33); //RandomCommittee RandomTree: InfoClassifier ic34 = new InfoClassifier(id++); RandomTree rtr4 = new RandomTree(); rtr4.setMinNum(2); rtr4.setAllowUnclassifiedInstances(true); RandomCommittee ccc23 = new RandomCommittee(); ccc23.setClassifier(rtr4); ic34.classifier = ccc23; ic34.name = "RandomComittee RandomTree"; ic34.properties.manageMissingValues = true; cls.add(ic34); //Class via Clustering: SimpleKMeans //N.B: it can't handle date attributes InfoClassifier ic35 = new InfoClassifier(id++); ClassificationViaClustering ccc24 = new ClassificationViaClustering(); SimpleKMeans km = new SimpleKMeans(); km.setNumClusters(original.trainingSet.numClasses()); ccc24.setClusterer(km); ic35.classifier = ccc24; ic35.name = "Classification via Clustering: KMeans"; ic35.properties.requireNumericDataset = true; cls.add(ic35); //Class via Clustering: FarthestFirst InfoClassifier ic36 = new InfoClassifier(id++); ClassificationViaClustering ccc25 = new ClassificationViaClustering(); FarthestFirst ff = new FarthestFirst(); ff.setNumClusters(original.trainingSet.numClasses()); ccc25.setClusterer(ff); ic36.classifier = ccc25; ic36.name = "Classification via Clustering: FarthestFirst"; ic36.properties.requireNumericDataset = true; cls.add(ic36); //SMO InfoClassifier ic37 = new InfoClassifier(id++); ic37.classifier = new SMO(); ic37.properties.requireNumericDataset = true; ic37.properties.manageMultiClass = false; ic37.name = "Smo"; cls.add(ic37); //Random Subspace InfoClassifier ic38 = new InfoClassifier(id++); RandomSubSpace sub = new RandomSubSpace(); sub.setClassifier(new REPTree()); ic38.classifier = sub; ic38.name = "Random Subspaces of RepTree"; ic38.properties.manageMissingValues = true; cls.add(ic38); //PART rule based InfoClassifier ic39 = new InfoClassifier(id++); PART p39 = new PART(); p39.setOptions(new String[] { "-C", "0.5" }); ic39.classifier = new PART(); ic39.name = "PART"; ic39.properties.manageMissingValues = true; cls.add(ic39); //Decision-Table / Naive Bayes InfoClassifier ic40 = new InfoClassifier(id++); ic40.classifier = new DTNB(); ic40.name = "DTNB"; ic40.properties.manageMissingValues = true; cls.add(ic40); //Ridor Rule based InfoClassifier ic41 = new InfoClassifier(id++); ic41.classifier = new Ridor(); ic41.name = "Ridor"; ic41.properties.manageMissingValues = true; cls.add(ic41); //Decision Table InfoClassifier ic42 = new InfoClassifier(id++); ic42.classifier = new DecisionTable(); ic42.name = "Decision Table"; ic42.properties.manageMissingValues = true; cls.add(ic42); //Conjunctive Rule InfoClassifier ic43 = new InfoClassifier(id++); ic43.classifier = new ConjunctiveRule(); ic43.name = "Conjunctive Rule"; ic43.properties.manageMissingValues = true; cls.add(ic43); //LogitBoost Decision Stump InfoClassifier ic44 = new InfoClassifier(id++); LogitBoost lb = new LogitBoost(); lb.setOptions(new String[] { "-L", "1.79" }); lb.setClassifier(new DecisionStump()); ic44.classifier = lb; ic44.name = "LogitBoost Decision Stump"; ic44.properties.manageMissingValues = true; cls.add(ic44); //Raced Incremental Logit Boost, Decision Stump InfoClassifier ic45 = new InfoClassifier(id++); RacedIncrementalLogitBoost rlb = new RacedIncrementalLogitBoost(); rlb.setClassifier(new DecisionStump()); ic45.classifier = rlb; ic45.name = "Raced Incremental Logit Boost, Decision Stumps"; ic45.properties.manageMissingValues = true; cls.add(ic45); //AdaboostM1 decision stump InfoClassifier ic46 = new InfoClassifier(id++); AdaBoostM1 adm = new AdaBoostM1(); adm.setClassifier(new DecisionStump()); ic46.classifier = adm; ic46.name = "AdaboostM1, Decision Stumps"; ic46.properties.manageMissingValues = true; cls.add(ic46); //AdaboostM1 J48 InfoClassifier ic47 = new InfoClassifier(id++); AdaBoostM1 adm2 = new AdaBoostM1(); adm2.setClassifier(new J48()); ic47.classifier = adm2; ic47.name = "AdaboostM1, J48"; ic47.properties.manageMissingValues = true; cls.add(ic47); //MultiboostAb Decision Table InfoClassifier ic48 = new InfoClassifier(id++); MultiBoostAB mba = new MultiBoostAB(); mba.setClassifier(new DecisionTable()); ic48.classifier = mba; ic48.name = "MultiboostAB, Decision Table"; ic48.properties.manageMissingValues = true; cls.add(ic48); //Multiboost NaiveBayes InfoClassifier ic49 = new InfoClassifier(id++); MultiBoostAB mba2 = new MultiBoostAB(); mba2.setClassifier(new NaiveBayes()); ic49.classifier = mba2; ic49.name = "MultiboostAB, Naive Bayes"; ic49.properties.manageMissingValues = true; cls.add(ic49); //Multiboost PART InfoClassifier ic50 = new InfoClassifier(id++); MultiBoostAB mba3 = new MultiBoostAB(); mba3.setClassifier(new PART()); ic50.classifier = mba3; ic50.name = "MultiboostAB, PART"; ic50.properties.manageMissingValues = true; cls.add(ic50); //Multiboost Random Tree InfoClassifier ic51 = new InfoClassifier(id++); MultiBoostAB mba4 = new MultiBoostAB(); RandomTree rtr3 = new RandomTree(); rtr3.setMinNum(2); rtr3.setAllowUnclassifiedInstances(true); mba4.setClassifier(rtr3); ic51.classifier = mba4; ic51.name = "MultiboostAB, RandomTree"; ic51.properties.manageMissingValues = true; cls.add(ic51); //Multiboost Rep Tree InfoClassifier ic52 = new InfoClassifier(id++); MultiBoostAB mba5 = new MultiBoostAB(); mba5.setClassifier(new REPTree()); ic52.classifier = mba5; ic52.name = "MultiboostAB, RepTree"; ic52.properties.manageMissingValues = true; cls.add(ic52); //Bagging Decision Stump InfoClassifier ic53 = new InfoClassifier(id++); Bagging bag = new Bagging(); bag.setClassifier(new DecisionStump()); ic53.classifier = bag; ic53.name = "Bagging Decision Stump"; ic53.properties.manageMissingValues = true; cls.add(ic53); //Bagging Decision Table InfoClassifier ic54 = new InfoClassifier(id++); Bagging bag1 = new Bagging(); bag1.setClassifier(new DecisionTable()); ic54.classifier = bag1; ic54.name = "Bagging Decision Table"; ic54.properties.manageMissingValues = true; cls.add(ic54); //Bagging HyperPipes InfoClassifier ic55 = new InfoClassifier(id++); Bagging bag2 = new Bagging(); bag2.setClassifier(new HyperPipes()); ic55.classifier = bag2; ic55.name = "Bagging Hyper Pipes"; cls.add(ic55); //Bagging Naive Bayes InfoClassifier ic56 = new InfoClassifier(id++); Bagging bag3 = new Bagging(); bag3.setClassifier(new NaiveBayes()); ic56.classifier = bag3; ic56.name = "Bagging Naive Bayes"; ic56.properties.manageMissingValues = true; cls.add(ic56); //Bagging PART InfoClassifier ic57 = new InfoClassifier(id++); Bagging bag4 = new Bagging(); bag4.setClassifier(new PART()); ic57.classifier = bag4; ic57.name = "Bagging PART"; ic57.properties.manageMissingValues = true; cls.add(ic57); //Bagging RandomTree InfoClassifier ic58 = new InfoClassifier(id++); Bagging bag5 = new Bagging(); RandomTree rtr2 = new RandomTree(); rtr2.setMinNum(2); rtr2.setAllowUnclassifiedInstances(true); bag5.setClassifier(rtr2); ic58.classifier = bag5; ic58.name = "Bagging RandomTree"; ic58.properties.manageMissingValues = true; cls.add(ic58); //NNge InfoClassifier ic59 = new InfoClassifier(id++); NNge nng = new NNge(); nng.setNumFoldersMIOption(1); nng.setNumAttemptsOfGeneOption(5); ic59.classifier = nng; ic59.name = "NNge"; cls.add(ic59); //OrdinalClassClassifier J48 InfoClassifier ic60 = new InfoClassifier(id++); OrdinalClassClassifier occ = new OrdinalClassClassifier(); occ.setClassifier(new J48()); ic60.classifier = occ; ic60.name = "OrdinalClassClassifier J48"; ic60.properties.manageMissingValues = true; cls.add(ic60); //Hyper Pipes InfoClassifier ic61 = new InfoClassifier(id++); ic61.classifier = new HyperPipes(); ic61.name = "Hyper Pipes"; cls.add(ic61); //Classification via Regression, M5P used by default InfoClassifier ic62 = new InfoClassifier(id++); ic62.classifier = new ClassificationViaRegression(); ic62.name = "Classification ViaRegression, M5P"; ic62.properties.requireNumericDataset = true; cls.add(ic62); //RBF Network InfoClassifier ic64 = new InfoClassifier(id++); RBFNetwork rbf = new RBFNetwork(); rbf.setRidge(0.00000001); //10^-8 rbf.setNumClusters(original.trainingSet.numAttributes() / 2); ic64.classifier = rbf; ic64.name = "RBF Network"; ic64.properties.requireNumericDataset = true; if (!original.properties.isStandardized) { ic64.properties.compatibleWithDataset = false; } cls.add(ic64); //RandomTree InfoClassifier ic66 = new InfoClassifier(id++); RandomTree rtr = new RandomTree(); rtr.setMinNum(2); rtr.setAllowUnclassifiedInstances(true); ic66.classifier = rtr; ic66.name = "Random Tree"; ic66.properties.manageMissingValues = true; cls.add(ic66); //RepTree InfoClassifier ic67 = new InfoClassifier(id++); REPTree rept = new REPTree(); ic67.classifier = rept; ic67.name = "Rep Tree"; ic67.properties.manageMissingValues = true; cls.add(ic67); //Decision Stump InfoClassifier ic68 = new InfoClassifier(id++); ic68.classifier = new DecisionStump(); ic68.name = "Decision Stump"; ic68.properties.manageMissingValues = true; cls.add(ic68); //OneR InfoClassifier ic69 = new InfoClassifier(id++); ic69.classifier = new OneR(); ic69.name = "OneR"; ic69.properties.requireNominalDataset = true; ic69.properties.manageMissingValues = true; cls.add(ic69); //LWL InfoClassifier ic71 = new InfoClassifier(id++); ic71.classifier = new LWL(); ic71.name = "LWL"; ic71.properties.manageMissingValues = true; cls.add(ic71); //Bagging LWL InfoClassifier ic72 = new InfoClassifier(id++); Bagging bg72 = new Bagging(); bg72.setClassifier(new LWL()); ic72.classifier = bg72; ic72.name = "Bagging LWL"; ic72.properties.manageMissingValues = true; cls.add(ic72); //Decorate InfoClassifier ic73 = new InfoClassifier(id++); ic73.classifier = new Decorate(); ic73.name = "Decorate"; ic73.properties.manageMissingValues = true; ic73.properties.minNumTrainingInstances = 15; this.indexDecorate = id - 1; cls.add(ic73); //Dagging InfoClassifier ic74 = new InfoClassifier(id++); Dagging dng = new Dagging(); dng.setClassifier(new SMO()); dng.setNumFolds(4); ic74.classifier = dng; ic74.properties.requireNumericDataset = true; ic74.properties.manageMultiClass = false; ic74.name = "Dagging SMO"; cls.add(ic74); //IB1 InfoClassifier ic75 = new InfoClassifier(id++); ic75.classifier = new IB1(); ic75.properties.manageMissingValues = true; ic75.name = "IB1"; cls.add(ic75); //Simple Logistic InfoClassifier ic76 = new InfoClassifier(id++); ic76.classifier = new SimpleLogistic(); ic76.properties.requireNumericDataset = true; ic76.name = "Simple Logistic"; cls.add(ic76); //VFI InfoClassifier ic77 = new InfoClassifier(id++); ic77.classifier = new VFI(); ic77.properties.manageMissingValues = true; ic77.name = "VFI"; cls.add(ic77); //check if classifier satisfies the constraints of min #instances checkMinNumInstanes(cls, original.trainingSet); return cls; }
From source file:gr.ntua.ece.cslab.panic.core.models.BagClassify.java
License:Apache License
public BagClassify() { super(); this.classifier = new Bagging(); }