weka - how to print incorrectly classified instances

Question

my weka output shows:

Correctly Classified Instances       32083               94.0244 %
Incorrectly Classified Instances      2039                5.9756 %

I want to be able to print out what the incorrect instances were so i can make adjustments and understand why they were misclassified.

my print method is below.
i am attempting to find instances whose predicted class value were not equal to the actual class value and then print its attributes.
but when i do this the attribute enumeration is not printing anything.

Does anyone have a suggestion for how to print out the misclassified instances?

thanks much.

private void printSummary(Classifier base, Evaluation eval, Instances data) throws Exception
{
    // output evaluation
    System.out.println();
    System.out.println("=== Setup ===");
    System.out.println("Classifier: " + classifierName.getClass().getName() + " " + Utils.joinOptions(base.getOptions()));
    System.out.println("Dataset: " + data.relationName());
    System.out.println();

    // output predictions
    System.out.println("# - actual - predicted - error - distribution - token");
    for (int i = 0; i < data.numInstances(); i++) 
    {
        double pred = base.classifyInstance(data.instance(i));
        double actual = data.instance(i).classValue();
        double[] dist = base.distributionForInstance(data.instance(i));

        if (pred != actual)
        {
            System.out.print((i+1));
            System.out.print(" - ");
            System.out.print(data.instance(i).toString(data.classIndex()));
            System.out.print(" - ");
            System.out.print(data.classAttribute().value((int) pred));
            System.out.print(" - ");
            if (pred != data.instance(i).classValue())
                System.out.print("yes");
            else
                System.out.print("no");
            System.out.print(" - ");
            System.out.print(Utils.arrayToString(dist));
            System.out.print(" - ");
            data.instance(i).enumerateAttributes().toString();
            System.out.println();
        }
    }

    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

Answer 1

I do this that way:

1. Train classifier.
2. For each instance I call 'classifier.explain'
3. If classification is incorrect I store them by incorrect probability (from worst error to least confident error)
4. Most confident error give me ideas what features should be added to classifier.