Learning Bayesian Belief Network Classifiers: Algorithms and System

Abstract. This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) – primarily unrestricted Bayesian networks and Bayesian multi-nets. We present our algorithms for learning these classifiers, and discuss how these methods address the overfitting problem and provide a natural method for feature subset selection. Using a set of standard classification problems, we empirically evaluate the performance of various BN-based classifiers. The results show that the proposed BN and Bayes multi-net classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. These results argue that BN based classifiers deserve more attention in the data mining community.

• Type: Conference
• Year: 2001
• Where: AI
• Authors: Jie Cheng, Russell Greiner
• Keywords: AI 2001, Artificial Intelligence, Bayes Multi-net Classifiers, BN Based Classifiers, Predictive Classifiers