Example usage for weka.classifiers.meta Stacking Stacking

Introduction

In this page you can find the example usage for weka.classifiers.meta Stacking Stacking.

Prototype

Stacking

Source Link

Usage

From source file:com.reactivetechnologies.platform.analytics.core.IncrementalClassifierBean.java

License:Open Source License

@Override
public RegressionModel ensembleModels(List<RegressionModel> models) {
    Stacking blend = new Stacking();
    //blend.setMetaClassifier(classifier);
    Classifier[] classifiers = new Classifier[models.size()];
    int i = 0;//from   ww  w .j a  va 2s  .com
    for (RegressionModel model : models) {
        classifiers[i++] = model.getTrainedClassifier();
    }
    blend.setClassifiers(classifiers);
    RegressionModel m = new RegressionModel();
    m.setTrainedClassifier(blend);
    return m;
}

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From source file:de.uniheidelberg.cl.swp.mlprocess.WEKARunner.java

License:Apache License

/**
 * Internal construction of the stacking classifier and its level 1 and level 0 algorithms.
 * //from  w  w w  .java 2  s.c  om
 * @param type A meta machine learning algorithm for level 1.
 * @param subtypes Multiple machine learning algorithms for level 0.
 * @param options Options for the classifiers.
 * @return The stacking classifier.
 */
private Stacking createStack(String type, String[] subtypes, String options) throws Exception {
    StringBuffer sb = new StringBuffer();
    Stacking stack = new Stacking();

    sb.append("-M " + getClass(Type.valueOf(type.toUpperCase())));

    for (String s : subtypes) {
        sb.append(" -B " + getClass(Type.valueOf(s.toUpperCase())));
    }
    sb.append(" " + options);

    Logging.getInstance().getLogger()
            .info("Building " + subtypes.length + " subclassifiers... this might take some time");
    stack.setOptions(Utils.splitOptions(sb.toString()));
    stack.buildClassifier(train);

    return stack;
}

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From source file:meddle.TrainModelByDomainOS.java

License:Open Source License

/**
 * Given the classifierName, return a classifier
 *
 * @param classifierName//from  w w  w.  ja  v  a2  s .  co  m
 *            e.g. J48, Bagging etc.
 */
public static Classifier getClassifier(String classifierName) {
    Classifier classifier = null;
    if (classifierName.equals("J48")) {
        J48 j48 = new J48();
        j48.setUnpruned(true);
        classifier = j48;
    } else if (classifierName.equals("AdaBoostM1")) {
        AdaBoostM1 adm = new AdaBoostM1();
        adm.setNumIterations(10);
        J48 j48 = new J48();
        adm.setClassifier(j48);
        classifier = adm;
    } else if (classifierName.equals("Bagging")) {
        Bagging bagging = new Bagging();
        bagging.setNumIterations(10);
        J48 j48 = new J48();
        bagging.setClassifier(j48);
        classifier = bagging;
    } else if (classifierName.equals("Stacking")) {
        Stacking stacking = new Stacking();
        stacking.setMetaClassifier(new Logistic());
        Classifier cc[] = new Classifier[2];
        cc[0] = new J48();
        cc[1] = new IBk();
        stacking.setClassifiers(cc);
        classifier = stacking;
    } else if (classifierName.equals("AdditiveRegression")) {
        AdditiveRegression ar = new AdditiveRegression();
        ar.setClassifier(new J48());
        classifier = ar;
    } else if (classifierName.equals("LogitBoost")) {
        LogitBoost lb = new LogitBoost();
        lb.setClassifier(new J48());
        classifier = lb;
    }
    return classifier;
}

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