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Learning Bayesian networks from data: an information-theory based approach
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Computers in Biology and Medicine
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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 FBC, 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 FBC learning. In our experiments, the FBC learning algorithm demonstrates better performance in both classification and ranking compared with other state-of-the-art learning algorithms. In addition, its reduced effort on structure learning makes its time complexity quite low as well.