combine weka models - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

combine weka models

Demo Code

/*/* w ww  .jav  a 2s. c o  m*/
 *  How to use WEKA API in Java 
 *  Copyright (C) 2014 
 *  @author Dr Noureddin M. Sadawi (noureddin.sadawi@gmail.com)
 *  
 *  This program is free software: you can redistribute it and/or modify
 *  it as you wish ... 
 *  I ask you only, as a professional courtesy, to cite my name, web page 
 *  and my YouTube Channel!
 *  
 */

package weka.api;

//import required classes
import weka.classifiers.Classifier;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.meta.AdaBoostM1;
import weka.classifiers.meta.Bagging;
import weka.classifiers.meta.Stacking;
import weka.classifiers.meta.Vote;
import weka.classifiers.trees.DecisionStump;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.RandomForest;
import weka.classifiers.trees.RandomTree;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

public class CombineModels {
    public static void main(String[] args) throws Exception {
        //load dataset
        String data = "/home/likewise-open/ACADEMIC/csstnns/Desktop/weather.nominal.arff";
        DataSource source = new DataSource(data);
        //get instances object 
        Instances trainingData = source.getDataSet();
        //set class index .. as the last attribute
        if (trainingData.classIndex() == -1) {
            trainingData.setClassIndex(trainingData.numAttributes() - 1);
        }

        /* Boosting a weak classifier using the Adaboost M1 method
         * for boosting a nominal class classifier
         * Tackles only nominal class problems
         * Improves performance
         * Sometimes overfits.
         */
        //AdaBoost .. 
        AdaBoostM1 m1 = new AdaBoostM1();
        m1.setClassifier(new DecisionStump());//needs one base-classifier
        m1.setNumIterations(20);
        m1.buildClassifier(trainingData);

        /* Bagging a classifier to reduce variance.
         * Can do classification and regression (depending on the base model)
         */
        //Bagging .. 
        Bagging bagger = new Bagging();
        bagger.setClassifier(new RandomTree());//needs one base-model
        bagger.setNumIterations(25);
        bagger.buildClassifier(trainingData);

        /*
         * The Stacking method combines several models
         * Can do classification or regression. 
         */
        //Stacking ..
        Stacking stacker = new Stacking();
        stacker.setMetaClassifier(new J48());//needs one meta-model
        Classifier[] classifiers = { new J48(), new NaiveBayes(),
                new RandomForest() };
        stacker.setClassifiers(classifiers);//needs one or more models
        stacker.buildClassifier(trainingData);

        /*
         * Class for combining classifiers.
         * Different combinations of probability estimates for classification are available. 
         */
        //Vote .. 
        Vote voter = new Vote();
        voter.setClassifiers(classifiers);//needs one or more classifiers
        voter.buildClassifier(trainingData);
    }
}

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