/**
* Buildclassifier selects a classifier from the set of classifiers by minimising error on the
* training data.
*
* @param data the training data to be used for generating the boosted classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifiers.length == 0) {
throw new Exception("No base classifiers have been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
newData.randomize(new Random(m_Seed));
if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1))
newData.stratify(m_NumXValFolds);
Instances train = newData; // train on all data by default
Instances test = newData; // test on training data by default
Classifier bestClassifier = null;
int bestIndex = -1;
double bestPerformance = Double.NaN;
int numClassifiers = m_Classifiers.length;
for (int i = 0; i < numClassifiers; i++) {
Classifier currentClassifier = getClassifier(i);
Evaluation evaluation;
if (m_NumXValFolds > 1) {
evaluation = new Evaluation(newData);
for (int j = 0; j < m_NumXValFolds; j++) {
train = newData.trainCV(m_NumXValFolds, j);
test = newData.testCV(m_NumXValFolds, j);
currentClassifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(currentClassifier, test);
}
} else {
currentClassifier.buildClassifier(train);
evaluation = new Evaluation(train);
evaluation.evaluateModel(currentClassifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println(
"Error rate: "
+ Utils.doubleToString(error, 6, 4)
+ " for classifier "
+ currentClassifier.getClass().getName());
}
if ((i == 0) || (error < bestPerformance)) {
bestClassifier = currentClassifier;
bestPerformance = error;
bestIndex = i;
}
}
m_ClassifierIndex = bestIndex;
m_Classifier = bestClassifier;
if (m_NumXValFolds > 1) {
m_Classifier.buildClassifier(newData);
}
}
