{37 () Bayesian Network Classiiers. *

Recent work in supervised learning has shown that a surprisingly simple Bayesian classiier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classiiers such as C4.5. This fact raises the question of whether a classiier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classiiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classiier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.