The Naive Bayesian Classifier Learning Algorithm based on Adaboost and Parameter Expectations

摘要：

Naive Bayesian classifier is a simple classification method based on Bayes statistics, which is one of the most popular classifiers and has been successfully applied to many fields. To improve the generalization ability of the naive Bayesian classifier, discriminative learning of the naive Bayesian classifier is researched. In this paper, a parameter learning algorithm AENB of the naive Bayesian classifier is proposed. This algorithm adopts the Adaboosts classifier ensemble framework, sequentially generates a series of individual classifiers with parameters, and obtains parameter expectations by summing the weighting parameters of each individual classifier. In the final, the naive Bayesian classifier with parameter expectations is constructed. The experimental results show that the AENB algorithm improves classification accuracy of the naive Bayesian classifier in the most cases. Furthermore, compared with the naive Bayesian classifier ensemble, AENB requires less space because there is no need to save parameters of individual classifiers.

关键词： classification Adaboost Naive Bayes parameter learning

作者: Hongbo Shi Xiaoyong Lv

作者单位: School of Information management,Shanxi University of Finance and economics,Taiyuan,030031 China

会议类型: 国际会议

会议名称: The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

会议地点: 黄山

会议语种:英文

页码: 377-381

在线出版日期: 2010-05-28（万方平台首次上网日期，不代表论文的发表时间）