An Incremental Structure Learning Approach for Bayesian Network

摘要：

　　Structure learning of Bayesian Network(BN)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 batch data in nature.It is difficult to learn the structure quickly from the huge amounts of data.But in many practical applications,the structure of BN should be learned by using time-series data that are available to us.To achieve this goal,we propose an incremental structure learning approach for BN.Firstly,we proposed the framework of incremental structure learning and a new evaluation criterion “ABIC(Adopt Bayesian Information Criterion)based on the BIC.Then,three phase algorithm is used to learn the structure.Numerical experiments on two standard networks show that our proposed algorithm can greatly improve the accuracy of the structure and the total of learning time is greatly reduced.

关键词： Bayesian Network Incremental Structure Learning ABIC Three phase algorithm

作者: Shuohao Li Jun Zhang Boliang Sun Jun Lei

作者单位: College of Information System and Management,National University of Defense Technology,Changsha 410073

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 4817-4822

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）