Full Bayesian network classifiers

The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we explore and represent variable independence in learning conditional probability tables (CPTs), instead of in learning structure. A full Bayesian network is used as the structure and a decision tree is learned for each CPT. The resulting model is called full Bayesian network classifiers (FBCs). In learning an F BC, learning the decision trees for CPTs captures essentially both variable independence and context-specific independence. We present a novel, efficient decision tree learning, which is also effective in the context of F BC learning. In our experiments, the F BC learning algorithm demonstrates better performance in both classification and ranking compared with other stateof-the-art learning algorithms. In addition, its reduced effort on structure learning makes its time complexity quite low as well. 1