A Comparison of Induction Algorithms for Selective andnon - Selective Bayesian Classi

In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classiier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, high-predictive-accuracy networks. We compare the performance of this classiier with selective and non-selective naive Bayesian classiiers. We show that the selective Bayesian network classi-er performs signiicantly better than both versions of the naive Bayesian classiier on almost all databases analyzed, and hence is an enhancement of the naive Bayesian classiier. Relative to the non-selective Bayesian network classiier, our selective Bayesian network classiier generates networks that are computationally simpler to evaluate and that display predictive accuracy comparable to that of Bayesian networks which model all features.