SubTree Augmented Na(i)ve Bayesian Classifier Based on the Fuzzy Equivalence Partition of Attribute Variables

摘要：

To make the structure of attribute variables in Na(i)ve Bayesian classifier (NB) or Tree Augmented Na(i)ve Bayesian classifier (TAN) more flexible and improve the accuracy of classification, a new Bayesian classifier called SubTree Augmented Na(i)ve Bayesian classifier (STAN) is proposed in this paper. It adopts the fuzzy equivalence partition approach to partition attribute variables into several subsets and admits the structure of attribute variables to be several subtrees. NB and TAN can be easily simulated by STAN as the threshold changes. Experiments with UCI datasets and synthetic datasets demonstrate STAN is effective and efficient.

作者: Hong-mei Chen Li-zhen Wang Wei-yi Liu Hao Chen

作者单位: Department of Computer Science and Engineering,School of Information Science and Engineering,Yunnan Hao Chen is with the Department of Computer Science and Engineering,School of Information Science an

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 1422-1426

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）