Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
The base-classifier-specific options must be passed to the base classifier after a double hyphen ("-").
java weka.classifiers.meta.CostSensitiveClassifier -W weka.classifiers.bayes.BayesNet -t train.arff -T test.arff - -Q weka.classifiers.bayes.net.search.global.HillClimber -E weka.classifiers.bayes.net.estimate.SimpleEstimator
Here ``-Q ...'' and ``-E ...'' are options to the base classifier.
Source: Weka FAQ