weka Combine ensemble - Java Machine Learning AI

Description

weka Combine ensemble
Demo Code

import java.io.FileWriter;
import java.util.ArrayList;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.evaluation.Prediction;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.classifiers.meta.AdaBoostM1;
import weka.classifiers.meta.Bagging;
import weka.classifiers.meta.Stacking;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.REPTree;
import weka.classifiers.trees.RandomForest;
import weka.core.Instances;
import weka.core.SelectedTag;
import weka.core.converters.ConverterUtils.DataSource;
import au.com.bytecode.opencsv.CSVWriter;
import weka.classifiers.meta.Vote;

public class Main {

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

        Instances train = DataSource
        read("./train1.arff");
        int cid1 = train.numAttributes() - 1;
        train.setClassIndex(cid1);/* w ww.  ja  v a2s  .co  m*/

        Instances validation = DataSource
        read("./validation1.arff");
        int cid2 = validation.numAttributes() - 1;
        validation.setClassIndex(cid2);

        Instances test = DataSource
        read("./test1.arff");
        int cid3 = test.numAttributes() - 1;
        test.setClassIndex(cid3);

        //adaboost J48
        J48 jtree1 = new J48();

        AdaBoostM1 btree1 = new AdaBoostM1();
        btree1.setClassifier(jtree1);
        //btree1.buildClassifier(train);

        //adaboost REPTree
        REPTree jtree2 = new REPTree();

        AdaBoostM1 btree2 = new AdaBoostM1();
        btree2.setClassifier(jtree2);
        //btree2.buildClassifier(train);

        //adaboost RF
        RandomForest jtree3 = new RandomForest();

        AdaBoostM1 btree3 = new AdaBoostM1();
        btree3.setClassifier(jtree3);
        //btree3.buildClassifier(train);

        //bagging J48
        J48 jtree4 = new J48();

        Bagging btree4 = new Bagging();
        btree4.setClassifier(jtree4);
        //btree4.buildClassifier(train);

        //Bagging REPTree
        REPTree jtree5 = new REPTree();

        Bagging btree5 = new Bagging();
        btree5.setClassifier(jtree5);
        //btree5.buildClassifier(train);

        //Bagging RF
        RandomForest jtree6 = new RandomForest();

        Bagging btree6 = new Bagging();
        btree6.setClassifier(jtree6);
        btree6.buildClassifier(train);

        //Stacking NB BJ48 BRF
        NaiveBayes NB7 = new NaiveBayes();
        J48 j48_tree7 = new J48();
        RandomForest RF7 = new RandomForest();
        Bagging b17 = new Bagging();
        b17.setClassifier(j48_tree7);
        Bagging b27 = new Bagging();
        b27.setClassifier(RF7);

        Stacking btree7 = new Stacking();
        Classifier[] classifiers7 = new Classifier[2];
        classifiers7[0] = b17;
        classifiers7[1] = b27;
        btree7.setClassifiers(classifiers7);
        btree7.setMetaClassifier(NB7);
        btree7.buildClassifier(train);

        //Stacking NB J48 MLP
        NaiveBayes NB8 = new NaiveBayes();
        J48 j48_tree8 = new J48();
        MultilayerPerceptron mp8 = new MultilayerPerceptron();

        Stacking btree8 = new Stacking();
        Classifier[] classifiers8 = new Classifier[2];
        classifiers8[0] = j48_tree8;
        classifiers8[1] = mp8;
        btree8.setClassifiers(classifiers8);
        btree8.setMetaClassifier(NB8);
        //btree8.buildClassifier(train);

        //VOTE: Combine multiple classifier
        Vote tree = new Vote();
        Classifier[] classifiers = { btree5 };
        //btree5,btree6,btree7

        tree.setClassifiers(classifiers);

        tree.addPreBuiltClassifier(btree7);
        tree.addPreBuiltClassifier(btree6);

        //combination criteria
        tree.setCombinationRule(new SelectedTag(Vote.AVERAGE_RULE,
                Vote.TAGS_RULES));

        tree.buildClassifier(train);

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(tree, validation);
        System.out.println(eval.toSummaryString("\nResults_RF\n\n", false));
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());

        ArrayList<Prediction> al = eval.predictions();
        ArrayList<String[]> as = new ArrayList<String[]>(al.size());
        for (int i = 0; i < al.size(); i++) {
            String[] s = new String[1];
            s[0] = al.get(i).toString();
            s[0] = s[0].substring(9, 11);
            as.add(s);
        }
        ArrayList<String[]> li = new ArrayList<String[]>(al.size());
        li.addAll(as);



        String csv = "./output.csv";
        CSVWriter writer = new CSVWriter(new FileWriter(csv));

        writer.writeAll(li);
        writer.close();
    }

}
weka Combine ensemble - Java Machine Learning AI

Description

Demo Code

Related Tutorials
