New algorithm for constructing Bayesian network structures from data

摘要：

Bayesian network is an uncertainty inference network based on probability. Its structure learning is one of the main research techniques in the field of data mining and knowledge discovering, while constructing Bayesian network structures from data is NP hard. According to the information theory and conditional independence test, a new algorithm is presented for the construction of optimal Bayesian network structure, and numerical experiments show that the structure with highest degree of data matching can be much faster determined by the new algorithm, thus the study of Bayesian network structures becomes more efficient.

作者: Xiao-Li Gao Bing-Han Li San-Yang Liu

作者单位: Department of Science Xi’Dian University Xi’an, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 360-364

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）