/** uses the filter */
protected static void useFilter(Instances data) throws Exception {
System.out.println("\n2. Filter");
weka.filters.supervised.attribute.AttributeSelection filter =
new weka.filters.supervised.attribute.AttributeSelection();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
filter.setEvaluator(eval);
filter.setSearch(search);
filter.setInputFormat(data);
Instances newData = Filter.useFilter(data, filter);
System.out.println(newData);
}
/** uses the low level approach */
protected static void useLowLevel(Instances data) throws Exception {
System.out.println("\n3. Low-level");
AttributeSelection attsel = new AttributeSelection();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
attsel.setEvaluator(eval);
attsel.setSearch(search);
attsel.SelectAttributes(data);
int[] indices = attsel.selectedAttributes();
System.out.println(
"selected attribute indices (starting with 0):\n" + Utils.arrayToString(indices));
}
/** uses the meta-classifier */
protected static void useClassifier(Instances data) throws Exception {
System.out.println("\n1. Meta-classfier");
AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
J48 base = new J48();
classifier.setClassifier(base);
classifier.setEvaluator(eval);
classifier.setSearch(search);
Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(classifier, data, 10, new Random(1));
System.out.println(evaluation.toSummaryString());
}
