Java Machine Learning Library (Java-ML)

Classification algorithms from Weka can be accessed from within Java-ML and used the same way as the native algorithms by using the WekaClassification bridge. This class can be wrapped around Weka classifiers and makes them transparently available to Java-ML based programs.

In the example below, we first load the iris data set. Next, we create a SMO support vector machine from Weka with default settings. Then, we wrap the SMO in the WekaClassifier bridge. Finally, we perform cross-validation on the classifier and write out the results.

/* Load data */
Dataset data = FileHandler.loadDataset(new File("iris.data"), 4, ",");
/* Create Weka classifier */
SMO smo = new SMO();
/* Wrap Weka classifier in bridge */
Classifier javamlsmo = new WekaClassifier(smo);
/* Initialize cross-validation */
CrossValidation cv = new CrossValidation(javamlsmo);
/* Perform cross-validation */
Map<Object, PerformanceMeasure> pm = cv.crossValidation(data);
/* Output results */
System.out.println(pm);