A Graph Partitioning Approach for Bayesian Network Structure Learning

摘要：

　　Structure learning of Bayesian Network is one of important topics in machine learning and widely applied in expert system.The traditional algorithms for structure learning are usually focused on the entire nodes in BN.It is difficult to learn the structure efficiently from the huge amounts of data.In reality,BN as a special inference network and the community also exists in BN.To achieve this goal,we propose Graph Partitioning Approach for BN Structure Learning.Firstly,we get the skeleton of BN by conditional dependence test.Secondly,skeleton is divided into some communities.Thirdly,the structure of every community is learned and the edges between communities are determined by BIC(Bayesian Information Criterion)score function.Numerical experiments on the standard network show that our proposed algorithm can greatly reduce the time cost of structure learning and have more accuracy.

关键词： Bayesian Network structure learning graph partitioning community

作者: LI Shuohao ZHANG Jun HUANG Kuihua GAO Chenxu

作者单位: College of Information System and Management,National University of Defense Technology,Changsha,Chin College of Information System and Management,National University of Defense Technology,Changsha,Chin

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 2887-2892

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）