performs attribute selection using CfsSubsetEval and GreedyStepwise and trains J48 - Java Machine Learning AI

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Description

performs attribute selection using CfsSubsetEval and GreedyStepwise and trains J48

Demo Code



import weka.attributeSelection.*;
import weka.core.*;
import weka.core.converters.ConverterUtils.*;
import weka.classifiers.*;
import weka.classifiers.meta.*;
import weka.classifiers.trees.*;
import weka.filters.*;

import java.util.*;

public class AttributeSelectionTest {

    protected static void useClassifier(Instances data) throws Exception {

        AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
        CfsSubsetEval eval = new CfsSubsetEval();
        GreedyStepwise search = new GreedyStepwise();
        search.setSearchBackwards(true);
        J48 base = new J48();
        classifier.setClassifier(base);//from  w  w  w . jav  a 2s . c om
        classifier.setEvaluator(eval);
        classifier.setSearch(search);
        Evaluation evaluation = new Evaluation(data);
        evaluation.crossValidateModel(classifier, data, 10, new Random(1));
        System.out.println(evaluation.toSummaryString());
    }

    protected static void useFilter(Instances data) throws Exception {

        weka.filters.supervised.attribute.AttributeSelection filter = new weka.filters.supervised.attribute.AttributeSelection();
        CfsSubsetEval eval = new CfsSubsetEval();
        GreedyStepwise search = new GreedyStepwise();
        search.setSearchBackwards(true);
        filter.setEvaluator(eval);
        filter.setSearch(search);
        filter.setInputFormat(data);
        Instances newData = Filter.useFilter(data, filter);
        System.out.println(newData);
    }

    protected static void useLowLevel(Instances data) throws Exception {
        System.out.println("\n3. Low-level");
        AttributeSelection attsel = new AttributeSelection();
        CfsSubsetEval eval = new CfsSubsetEval();
        GreedyStepwise search = new GreedyStepwise();
        search.setSearchBackwards(true);
        attsel.setEvaluator(eval);
        attsel.setSearch(search);
        attsel.SelectAttributes(data);
        int[] indices = attsel.selectedAttributes();
        System.out
                .println("selected attribute indices (starting with 0):\n"
                        + Utils.arrayToString(indices));
    }

    public static void main(String[] args) throws Exception {
        System.out.println("\n0. Loading data");
        DataSource source = new DataSource(args[0]);
        Instances data = source.getDataSet();
        if (data.classIndex() == -1)
            data.setClassIndex(data.numAttributes() - 1);
        useClassifier(data);
        useFilter(data);
        useLowLevel(data);
    }
}

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