Journal of Systems Engineering and Electronics ›› 2012, Vol. 34 ›› Issue (12): 2585-2591.doi: 10.3969/j.issn.1001-506X.2012.12.32

• 软件、算法与仿真 • 上一篇    下一篇

融合先验信息的贝叶斯网络结构学习方法

吴红,王维平,杨峰   

  1. 国防科学技术大学信息系统与管理学院, 湖南 长沙 410073
  • 出版日期:2012-12-25 发布日期:2010-01-03

Structure learning method of Bayesian network with prior information

WU Hong,WANG Wei-ping,YANG Feng   

  1. School of Information Systems and Management, National University of DefenseTechnology, Changsha 410073, China
  • Online:2012-12-25 Published:2010-01-03

摘要:

在贝叶斯网络结构学习的过程中,如何采集先验信息并合理利用它对于构建准确的网络结构非常重要。鉴于此,依据有先验信息的贝叶斯网络结构学习的三个环节:先验信息的采集、先验信息的融合和网络结构的优化,首先讨论了现有先验信息获取方法的不足,并提出了基于信念图的先验信息获取方法;其次针对所获取的先验信息通常具有一定的不确性,对最小描述长度测度进行了改进以融合非确定性先验信息;最后依据问题特性对模拟退火算法进行了适当的修改以更好地优化网络结构。实验表明,提出的结构学习方法能够有效地提高网络结构的学习精度。

Abstract:

In the process of Bayesian network structure learning, how to gather prior information and use it effectively are very important for building an exact network structure. Therefore, according to the three stages of Bayesian network structure learning with prior information: information gathering, information fusion and the optimization of learned network structure. Firstly, the deficiency of currently available methods that is used for obtaining prior information is discussed, and then a new method of gathering prior structure information based on socalled belief map is proposed. Secondly, the minimum description length score is modified so that it can fuse uncertain prior information. Finaly, a simulated annealing method is revised appropriately according to the characteristics of the problem for searching the optimal structure. Experimental results show that the proposed method can improve the precision of structure learning efficiently.

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