Use weka aggregation classifiers - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

Use weka aggregation classifiers

Demo Code

import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.functions.Logistic;
import weka.classifiers.meta.AdaBoostM1;
import weka.classifiers.meta.Bagging;
import weka.classifiers.meta.Stacking;
import weka.classifiers.meta.Vote;
import weka.classifiers.trees.DecisionStump;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.RandomForest;
import weka.classifiers.trees.RandomTree;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.Classifier;

public class aggregation {
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource("weather.arff");
        Instances traindata = source.getDataSet();
        traindata.setClassIndex(traindata.numAttributes() - 1);
        /**//from  www.j  ava  2s  .c  o m
         * AdaBoost
         */
        AdaBoostM1 m1 = new AdaBoostM1();
        m1.setClassifier(new DecisionStump());
        m1.setNumIterations(100);
        m1.buildClassifier(traindata);
        /**
         * bagging
         */
        Bagging bagging = new Bagging();
        bagging.setClassifier(new RandomTree());
        bagging.setNumIterations(25);
        bagging.buildClassifier(traindata);
        /**
         * stacking
         */
        Stacking stack = new Stacking();
        stack.setMetaClassifier(new Logistic());
        Classifier[] classifiers = { new J48(), new NaiveBayes(),
                new RandomForest() };
        stack.setClassifiers(classifiers);
        stack.buildClassifier(traindata);
        /**
         * voting
         */
        Vote vote = new Vote();
        vote.setClassifiers(classifiers);
        vote.buildClassifier(traindata);

    }

}

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