Example usage for weka.classifiers.meta Stacking setClassifiers

Introduction

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

Prototype

public void setClassifiers(Classifier[] classifiers) 

Source Link

Document

Sets the list of possible classifers to choose from.

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;/* w w  w.j a v  a  2s.co  m*/
    for (RegressionModel model : models) {
        classifiers[i++] = model.getTrainedClassifier();
    }
    blend.setClassifiers(classifiers);
    RegressionModel m = new RegressionModel();
    m.setTrainedClassifier(blend);
    return m;
}

---

From source file:meddle.TrainModelByDomainOS.java

License:Open Source License

/**
 * Given the classifierName, return a classifier
 *
 * @param classifierName// w ww.j  a  v a 2s.c om
 *            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;
}

---