关键词: 贝叶斯网络分类器, 集成方法, 参数学习, 判别式学习

Abstract: Concerning the characteristics of Bayesian networks classifier, a discriminative parameter learning algorithm of Bayesian networks classifier based on parameters ensemble named PEBNC was proposed to improve the classification performance of Bayesian classifier. This algorithm regarded the parameter learning as a regression problem, applied the additive regression model to the parameter learning of Bayesian networks classifier, and realized a discriminative parameter learning of Bayesian networks classifier. The experimental results indicate that the PEBNC classifier can improve the classification performance in most cases. Furthermore, compared with the general Bayesian classifier ensemble, PEBNC requires less space because there is no need to save parameters of individual classifiers.

Key words: Bayesian network classifier, ensemble method, parameter learning, discriminative learning