Example usage for weka.classifiers.trees J48 J48

Introduction

In this page you can find the example usage for weka.classifiers.trees J48 J48.

Prototype

J48

Source Link

Usage

From source file:mulan.classifier.meta.MultiLabelMetaLearner.java

License:Open Source License

/**
 * Creates a new instance of {@link MultiLabelMetaLearner} with default
 * {@link LabelPowerset} multi-label classifier using J48 as the base
 * classifier./*from   w w w. jav a2 s  .co m*/
 * @throws Exception 
 */
public MultiLabelMetaLearner() throws Exception {
    this(new LabelPowerset(new J48()));
}

From source file:mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer.java

License:Open Source License

/**
 * Default constructor
 */
public ExampleBasedFMeasureOptimizer() {
    this(new BinaryRelevance(new J48()));
}

From source file:mulan.classifier.meta.thresholding.MLPTO.java

License:Open Source License

/**
 * Default constructor
 */
public MLPTO() {
    this(new BinaryRelevance(new J48()), new HammingLoss());
}

From source file:mulan.classifier.transformation.Pairwise.java

License:Open Source License

/**
 * Default constructor using J48 as underlying classifier
 */
public Pairwise() {
    this(new J48());
}

From source file:mulan.classifier.transformation.TransformationBasedMultiLabelLearner.java

License:Open Source License

/**
 * Creates a new instance of {@link TransformationBasedMultiLabelLearner} with default
 * {@link J48} base classifier./*from  w  w  w.  j a v a2s  . c  om*/
 */
public TransformationBasedMultiLabelLearner() {
    this(new J48());
}

From source file:mulan.classifier.transformation.TwoStageClassifierChainArchitecture.java

License:Open Source License

/**
 * Default constructor using J48 as underlying classifier
 */
public TwoStageClassifierChainArchitecture() {
    super(new J48());
}

From source file:mulan.classifier.transformation.TwoStagePrunedClassifierChainArchitecture.java

License:Open Source License

/**
 * Default constructor using J48 as underlying classifier
 */
public TwoStagePrunedClassifierChainArchitecture() {
    super(new J48());
}

From source file:mulan.classifier.transformation.TwoStageVotingArchitecture.java

License:Open Source License

/**
 * Default constructor using J48 as underlying classifier
 */
public TwoStageVotingArchitecture() {
    super(new J48());
}

From source file:mulan.examples.GettingPredictionsOnUnlabeledData.java

License:Open Source License

/**
 * Executes this example//from www. j a v a  2 s.com
 *
 * @param args command-line arguments -arff, -xml and -unlabeled
 */
public static void main(String[] args) {

    try {
        String arffFilename = Utils.getOption("arff", args);
        String xmlFilename = Utils.getOption("xml", args);
        System.out.println("Loading the training data set...");
        MultiLabelInstances trainingData = new MultiLabelInstances(arffFilename, xmlFilename);

        RAkEL model = new RAkEL(new LabelPowerset(new J48()));

        System.out.println("Building the model...");
        model.build(trainingData);

        String unlabeledDataFilename = Utils.getOption("unlabeled", args);
        System.out.println("Loading the unlabeled data set...");
        MultiLabelInstances unlabeledData = new MultiLabelInstances(unlabeledDataFilename, xmlFilename);

        int numInstances = unlabeledData.getNumInstances();
        for (int instanceIndex = 0; instanceIndex < numInstances; instanceIndex++) {
            Instance instance = unlabeledData.getDataSet().instance(instanceIndex);
            MultiLabelOutput output = model.makePrediction(instance);
            if (output.hasBipartition()) {
                String bipartion = Arrays.toString(output.getBipartition());
                System.out.println("Predicted bipartion: " + bipartion);
            }
            if (output.hasRanking()) {
                String ranking = Arrays.toString(output.getRanking());
                System.out.println("Predicted ranking: " + ranking);
            }
            if (output.hasConfidences()) {
                String confidences = Arrays.toString(output.getConfidences());
                System.out.println("Predicted confidences: " + confidences);
            }
        }
    } catch (InvalidDataFormatException e) {
        System.err.println(e.getMessage());
    } catch (Exception ex) {
        Logger.getLogger(GettingPredictionsOnUnlabeledData.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:mulan.examples.StoringAndLoadingModels.java

License:Open Source License

public static void main(String[] args) {
    try {/*from w ww  .jav  a 2  s .co  m*/
        String trainingDataFilename = Utils.getOption("train", args);
        String testingDataFilename = Utils.getOption("test", args);
        String labelsFilename = Utils.getOption("labels", args);
        System.out.println("Loading the training data set...");
        MultiLabelInstances trainingData = new MultiLabelInstances(trainingDataFilename, labelsFilename);
        System.out.println("Loading the testing data set...");
        MultiLabelInstances testingData = new MultiLabelInstances(testingDataFilename, labelsFilename);
        BinaryRelevance learner1 = new BinaryRelevance(new J48());

        String modelFilename = Utils.getOption("model", args);
        System.out.println("Building the model...");
        learner1.build(trainingData);

        System.out.println("Storing the model...");
        SerializationHelper.write(modelFilename, learner1);

        System.out.println("Loading the model...");
        BinaryRelevance learner2;
        learner2 = (BinaryRelevance) (MultiLabelLearner) SerializationHelper.read(modelFilename);
        Evaluator evaluator = new Evaluator();
        Evaluation evaluation;
        evaluation = evaluator.evaluate(learner2, testingData);
        System.out.println(evaluation);
    } catch (Exception ex) {
        Logger.getLogger(StoringAndLoadingModels.class.getName()).log(Level.SEVERE, null, ex);
    }
}