A New Algorithm for Learning Bayesian Classifiers from Data
Alexander Kleiner, Bernadette Sharp · 2000
We introduce a new algorithm for the induction of classi ers from data, based on Bayesian networks. Basically this problem has already been examined from two perspectives: rst, the induction of classi ers by learning algorithms for Bayesian networks, second, the induction of classi ers based on the naive Bayesian classi er. Our approach is located between these two perspectives; it eliminates the disadvantages of both while exploiting their advantages. In contrast to recently appeared re nements of the naive Bayes classi er, which captures single correlations in the data, we have developed an approach which captures multiple correlations and furthermore does a trade-o between complexity and accuracy. In this paper we evaluate the implementation of our approach with data sets from the machine learning repository and data sets arti cially generated by Bayesian networks.