tuning weka classifiers meta AdaBoostM1 - Java Machine Learning AI

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Description

tuning weka classifiers meta AdaBoostM1

Demo Code

import weka.classifiers.Evaluation;
import weka.classifiers.meta.AdaBoostM1;
import weka.classifiers.meta.CVParameterSelection;
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 AdaBoost {

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

        // load data sets
        Instances train = new Instances(new BufferedReader(new FileReader(
                "/sonar_train.arff")));
        Instances test = new Instances(new BufferedReader(new FileReader(
                "/sonar_test.arff")));
        train.setClassIndex(train.numAttributes() - 1);
        test.setClassIndex(test.numAttributes() - 1);

        //Classifier [] ClassifierArray=new Classifier[3];
        //ClassifierArray[1]=new J48();
        //ClassifierArray[0]=new NaiveBayes();
        //ClassifierArray[2]=new NBTree();
        CVParameterSelection ps = new CVParameterSelection();
        AdaBoostM1 vs = new AdaBoostM1();
        //String[] options=new String[3];
        //options[2]="-R MAJ";
        //options[1]="-B weka.classifiers.functions.SMO -B weka.classifiers.bayes.NaiveBayes";
        //options[0]="-S <2>";
        //vs.setOptions(options);
        ps.setOptions(weka.core.Utils/*from   w  w w.j  a  va  2s.co m*/
                .splitOptions("-P \"I 1.0 10.0 1.0\" -P \"P 1.0 100.0 10.0\" -W \"weka.classifiers.meta.AdaBoostM1\" -- -P 100 -S 1 -I 10 -W \"weka.classifiers.functions.SMO\" -- -V -1 -C 1 -P 1.0E-12"));

        ps.buildClassifier(train);

        System.out
                .println(Utils.joinOptions(ps.getBestClassifierOptions()));
        String[] options = ps.getBestClassifierOptions();

        vs.setOptions(options);
        System.out.println(Utils.joinOptions(vs.getOptions()));
        //vs.setClassifiers(ClassifierArray);

        vs.buildClassifier(train);
        //find optimal parameter

        //Dagging cls = new Dagging();
        //change the base classifier
        //cls.setClassifier(new NBTree());
        //change the parameter for dagging
        //cls.setNumFolds(1);
        //cls.setSeed(7);
        //cls.buildClassifier(train);
        //System.out.println(vs.getCombinationRule());
        //System.out.println(vs.getOptions());
        PrintWriter pw = new PrintWriter(new FileWriter(
                "/sonar1.txt"));

        //System.out.println(Utils.joinOptions(ps.getBestClassifierOptions()));
        for (int i = 0; i < test.numInstances(); i++) {
            double pred = vs.classifyInstance(test.instance(i));
            pw.println(pred);
        }
        pw.close();
        //weka.core.SerializationHelper.write("/ionosphere.model", vs);
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(vs, test);
        Double error_c = eval.errorRate();
        System.out.println(error_c);

    }
}

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