com.mycompany.id3classifier.ID3Shell.java Source code

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Here is the source code for com.mycompany.id3classifier.ID3Shell.java

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package com.mycompany.id3classifier;

import java.util.Random;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.Id3;
import weka.core.Instances;
import weka.core.converters.ConverterUtils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.Standardize;

/**
 *
 * @author Besseym
 */
public class ID3Shell {
    public static void main(String[] args) throws Exception {
        ConverterUtils.DataSource source = new ConverterUtils.DataSource("lensesData.csv");
        Instances dataSet = source.getDataSet();

        Discretize filter = new Discretize();
        filter.setInputFormat(dataSet);
        dataSet = Filter.useFilter(dataSet, filter);

        Standardize standardize = new Standardize();
        standardize.setInputFormat(dataSet);
        dataSet = Filter.useFilter(dataSet, standardize);

        dataSet.setClassIndex(dataSet.numAttributes() - 1);
        dataSet.randomize(new Random(9001)); //It's over 9000!!

        int folds = 10;
        //Perform crossvalidation
        Evaluation eval = new Evaluation(dataSet);
        for (int n = 0; n < folds; n++) {
            int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
            int testSize = dataSet.numInstances() - trainingSize;

            Instances trainingData = dataSet.trainCV(folds, n);
            Instances testData = dataSet.testCV(folds, n);

            ID3Classifier classifier = new ID3Classifier();
            // Id3 classifier = new Id3();
            classifier.buildClassifier(trainingData);

            eval.evaluateModel(classifier, testData);
        }
        System.out.println(eval.toSummaryString("\nResults:\n", false));
    }
}