Examples of weka.classifiers.meta.CostSensitiveClassifier

weka.classifiers.meta.CostSensitiveClassifier

A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.

Valid options are:

 -M Minimize expected misclassification cost. Default is to reweight training instances according to costs per class

 -C <cost file name> File name of a cost matrix to use. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -N option.

 -N <directory> Name of a directory to search for cost files when loading costs on demand (default current directory).

 -cost-matrix <matrix> The cost matrix in Matlab single line format.

 -S <num> Random number seed. (default 1)

 -D If set, classifier is run in debug mode and may output additional info to the console

 -W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)

  Options specific to classifier weka.classifiers.rules.ZeroR:

 -D If set, classifier is run in debug mode and may output additional info to the console

Options after -- are passed to the designated classifier.

@author Len Trigg (len@reeltwo.com) @version $Revision: 1.29 $

        smo.setBuildLogisticModels(isBuildLogisticModel());


        Classifier classifier = smo;


        if (useCostTraining) {
            CostSensitiveClassifier cost = new CostSensitiveClassifier();
            cost.setClassifier(smo);
            CostMatrix matrix = new CostMatrix(2);
            matrix.setElement(0, 0, 0);
            matrix.setElement(0, 1, falsePositiveCost);
            matrix.setElement(1, 0, falseNegativeCost);
            matrix.setElement(1, 1, 0);
            cost.setCostMatrix(matrix);


            classifier = cost;


            System.out.print("Wrapping SMO in CostSensitiveClassifier " + matrix.toMatlab());


            if (useReweighting) {
                cost.setMinimizeExpectedCost(false);
                System.out.println(" using re-weighting.");
            } else {
                cost.setMinimizeExpectedCost(true);
                System.out.println(" using min-cost criterion.");
            }
        }


        try {

Examples of weka.classifiers.meta.CostSensitiveClassifier

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