Use weka Vote - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

Use weka Vote

Demo Code

import weka.classifiers.Evaluation;
import weka.classifiers.meta.GridSearch;
import weka.classifiers.meta.Vote;
import weka.core.Instances;
import weka.core.Utils;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.PrintWriter;

public class Voting {

    public static void main(String[] args) throws Exception {

        Instances train = new Instances(new BufferedReader(new FileReader(
                "spambase_train.arff")));
        Instances test = new Instances(new BufferedReader(new FileReader(
                "spambase_test.arff")));
        train.setClassIndex(train.numAttributes() - 1);
        test.setClassIndex(test.numAttributes() - 1);

        Vote vs = new Vote();
        GridSearch ps = new GridSearch();
        vs.setOptions(weka.core.Utils/* w ww.  j  a  v  a2  s. c  om*/
                .splitOptions("-B \"weka.classifiers.functions.SMO -C 1 -L 0.01 -P 1E-10\" -B \"weka.classifiers.trees.NBTree\" -B \"weka.classifiers.trees.RandomForest -I 10 -K 20 -depth 5\" -R MAJ"));

        System.out.println(Utils.joinOptions(vs.getOptions()));
        vs.buildClassifier(train);
        PrintWriter pw = new PrintWriter(new FileWriter(
                "spambase-L5.txt"));
        for (int i = 0; i < test.numInstances(); i++) {
            double pred = vs.classifyInstance(test.instance(i));
            pw.println(pred);
        }
        pw.close();
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(vs, test);
        Double error_c = eval.errorRate();
        System.out.println(error_c);

    }
}

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