weka tree classifier - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

weka tree classifier

Demo Code



import weka.core.Instances;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.Writer;
import java.util.Random;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.trees.J48;

import weka.core.SparseInstance;

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

    BufferedReader reader = new BufferedReader(new FileReader("test.arff"));
    Instances data = new Instances(reader);
    reader.close();// w  w w. j  a va 2  s .  c o m
    data.setClassIndex(data.numAttributes() - 1);
    int i = data.numInstances();
    int j = data.numAttributes() - 1;
    File file = new File("tablej48.csv");
    Writer output = null;
    output = new BufferedWriter(new FileWriter(file));
    output.write("%missing,auc,correct,fmeasure\n");

    int numBlock = data.numInstances();
    Random randomGenerator = new Random();
    int c = randomGenerator.nextInt(j);
    for (int perc = 0; perc < 101; perc = perc + 5) {
      Instances mdata = new Instances(data);
      int numMissing = perc * numBlock / 100;
      for (int k = 0; k < numMissing; k++) {
        int r = randomGenerator.nextInt(i);

        mdata.instance(r).setMissing(c);
      }
      Classifier cModel = (Classifier) new J48();
      Evaluation eTest = new Evaluation(mdata);
      eTest.crossValidateModel(cModel, mdata, 10, mdata.getRandomNumberGenerator(1));
      double y1 = eTest.areaUnderROC(0);
      double y2 = eTest.correct();
      double y3 = eTest.fMeasure(0);
      output.write(perc + "," + y1 + "," + y2 + "," + y3 + "\n");
      // mdata=data;
    }
    output.close();

  }
}

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