Cost Sensitive Classifier Random Forest Java in weka

Cost Sensitive Classifier Random Forest Java in weka. The code also does a re-sampling to the training data in the cross validation.
java code

package wekatest;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import weka.classifiers.CostMatrix;
import weka.classifiers.Evaluation;
import weka.classifiers.meta.CostSensitiveClassifier;

import weka.classifiers.trees.RandomForest;
import weka.core.Instances;
import weka.core.converters.ArffSaver;
import weka.filters.Filter;
import weka.filters.supervised.instance.Resample;
import weka.filters.unsupervised.instance.Randomize;

/**
 *
 * @author samy
 */
public class WekaTestRFResampleCost {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) throws Exception {
        BufferedReader br = null;
        double numFolds = 10.0d;
        double precisionOne = 0.0d;
        double recallOne = 0.0d;
        double fmeansureOne = 0.0d;
        double precisionTwo = 0.0d;
        double recallTwo = 0.0d;
        double fmeansureTwo = 0.0d;



        double ROCone = 0.0d;
        double ROCtwo = 0.0d;



        double PRCone = 0.0d;
        double PRCtwo = 0.0d;




        br = new BufferedReader(new FileReader("D:\\CostResample\\eclipse.arff"));
        ArffSaver saverTets = new ArffSaver();
        ArffSaver saverTraining = new ArffSaver();
        Instances trainData = new Instances(br);
        trainData.setClassIndex(trainData.numAttributes() - 1);
        br.close();
        Randomize randFilterMain = new Randomize();
        randFilterMain.setInputFormat(trainData);
        trainData = Filter.useFilter(trainData, randFilterMain);
        int size = (int) (trainData.numInstances() / numFolds);
        int begin = 0; // is index if flod.
        int end = size - 1; // is index 

        System.out.println("Total Size of instances" + trainData.numInstances() + " , flod size=" + size);
        for (int i = 1; i <= numFolds; i++) {
            System.out.println("Iteration # " + i + " Begin =" + begin + " , end=" + end);
            Instances tempTraining = new Instances(trainData);
            Instances tempTesting = new Instances(trainData, begin, (end - begin));
            for (int j = 0; j < (end - begin); j++) {
                tempTraining.delete(begin);
            }

            //// Filters 
            Resample resample = new Resample();

            resample.setBiasToUniformClass(0.5f);
            resample.setInvertSelection(false);
            resample.setNoReplacement(false);

            resample.setRandomSeed(1);
            //smoteFilter.setClassValue("2");
            resample.setInputFormat(tempTraining);

            System.out.println("Number of instances before filter " + tempTraining.numInstances());

            Instances resmapleTempTraining = Filter.useFilter(tempTraining, resample);



            System.out.println("Number of instances after filter " + resmapleTempTraining.numInstances());

            RandomForest randomForest = new RandomForest();
            randomForest.setNumTrees(100);

            System.out.println("Started building the model #" + i);
//            randomForest.buildClassifier(resmapleTempTraining);

            CostSensitiveClassifier costSensitiveClassifier = new CostSensitiveClassifier();
            CostMatrix costMatrix = new CostMatrix(2);
//          costMatrix.setCell(0, 0, 0.8d);
            //          costMatrix.setCell(0, 1, 5.0d);
            costMatrix.setCell(1, 0, 2d);
//            costMatrix.setCell(1, 1, 1.0d);

            costSensitiveClassifier.setClassifier(randomForest);
            costSensitiveClassifier.setCostMatrix(costMatrix);
            costSensitiveClassifier.buildClassifier(resmapleTempTraining);

            saverTraining.setInstances(resmapleTempTraining);
            saverTraining.setFile(new File("D:\\SumCost\\eclipse\\" + i + "_training.arff"));
            saverTets.setInstances(tempTesting);
            saverTets.setFile(new File("D:\\SumCost\\eclipse\\" + i + "_testing.arff"));

            saverTraining.writeBatch();
            saverTets.writeBatch();


            System.out.println("Done with building the model");

            Evaluation evaluation = new Evaluation(tempTesting);

            evaluation.evaluateModel(costSensitiveClassifier, tempTesting);

            System.out.println("Results For Class -1- ");
            System.out.println("Precision=  " + evaluation.precision(0));
            System.out.println("Recall=  " + evaluation.recall(0));
            System.out.println("F-measure=  " + evaluation.fMeasure(0));
            System.out.println("ROC=  " + evaluation.areaUnderROC(0));
            System.out.println("Results For Class -2- ");
            System.out.println("Precision=  " + evaluation.precision(1));
            System.out.println("Recall=  " + evaluation.recall(1));
            System.out.println("F-measure=  " + evaluation.fMeasure(1));
            System.out.println("ROC=  " + evaluation.areaUnderROC(1));
            precisionOne += evaluation.precision(0);
            recallOne += evaluation.recall(0);
            fmeansureOne += evaluation.fMeasure(0);
            precisionTwo += evaluation.precision(1);
            recallTwo += evaluation.recall(1);
            fmeansureTwo += evaluation.fMeasure(1);



            ROCone += evaluation.areaUnderROC(0);
            ROCtwo += evaluation.areaUnderROC(1);

            PRCone += evaluation.areaUnderPRC(0);
            PRCtwo += evaluation.areaUnderPRC(1);


            begin = end + 1;
            end += size;
            if (i == (numFolds - 1)) {
                end = trainData.numInstances();
            }
        }




        System.out.println("####################################################");
        System.out.println("Results For Class -1- ");
        System.out.println("Precision=  " + precisionOne / numFolds);
        System.out.println("Recall=  " + recallOne / numFolds);
        System.out.println("F-measure=  " + fmeansureOne / numFolds);
        System.out.println("ROC=  " + ROCone / numFolds);
        System.out.println("PRC= " + PRCone / numFolds);



        System.out.println("Results For Class -2- ");
        System.out.println("Precision=  " + precisionTwo / numFolds);
        System.out.println("Recall=  " + recallTwo / numFolds);
        System.out.println("F-measure=  " + fmeansureTwo / numFolds);
        System.out.println("ROC=  " + ROCtwo / numFolds);
        System.out.println("PRC= " + PRCtwo / numFolds);



    }
}

updated.