Discriminative vs. Generative Learning of Bayesian Network Classifiers

Abstract

Discriminative learning of Bayesian network classifiers has recently received considerable attention from the machine learning community. This interest has yielded several publications where new methods for the discriminative learning of both structure and parameters have been proposed. In this paper we present an empirical study used to illustrate how discriminative learning performs with respect to generative learning using simple Bayesian network classifiers such as naive Bayes or TAN, and we discuss when and why a discriminative learning is preferred. We also analyzed how log-likelihood and conditional log-likelihood scores guide the learning process of Bayesian network classifiers.KeywordsBayesian NetworkJoint Probability DistributionGenerative LearningDataset SizeDiscriminative MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.