Learning Hidden Variables in Bayesian Networks with Bayesian Entropy Criterion for Supervised Classification

摘要：

In this paper, we make use of a new criterion, the Bayesian Entropy Criterion (BEC), to learn hidden variable Bayesian Networks for supervised classification. This criterion takes into account the decisional purpose of a model by minimizing the integrated classification entropy. Experiments on real dataset show that BEC performs better than the BIC criterion to select a model minimizing the classification error rate. Learning hidden variable structures with BEC, we can find the more effective hidden variables for supervised classification model, which may reveal some valuable principles of certain domain.

作者: Xiangyang Wang Lei Wang Wanggen Wan Xiaoqin Yu

作者单位: School of Communication and Information Engineering, Shanghai University, Shanghai, China

会议类型: 国际会议

会议名称: 2010 International Conference on Audio,Language and Image Processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

会议地点: 上海

会议语种:英文

页码: 6-11

在线出版日期: 2010-11-23（万方平台首次上网日期，不代表论文的发表时间）