Journal of Systems Engineering and Electronics ›› 2012, Vol. 34 ›› Issue (10): 2157-2162.doi: 10.3969/j.issn.1001-506X.2012.10.31

• 软件、算法与仿真 • 上一篇    下一篇

贝叶斯网络参数学习中的连续变量离散化方法

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:2012-10-19 发布日期:2010-01-03

Discretization method of continuous variables in Bayesian network parameter learning

WU Hong, WANG Wei-ping, YANG Feng   

  1. School of Information System and Management, National University of Defense 
    Technology, Changsha 410073, China
  • Online:2012-10-19 Published:2010-01-03

摘要:

首先从离散方案对推理功能的影响出发,提出将条件信息熵作为评判离散方案好坏的标准;其次从降低问题求解的复杂度出发,提出将贝叶斯网络划分为多个极小简单子网分别进行离散化;最后,依据离散化问题与路径规划问题的相似性,设计了一套利用蚁群算法进行问题求解的方法。实验表明,采用所提方法进行贝叶斯网络连续变量离散化,能很好地将连续变量的取值空间进行分类,从而达到良好的推理效果。

Abstract:

First, based on the influence of the discretization scheme on the inference function, the conditional information entropy is proposed to evaluate the discretization scheme; second, in order to reduce the complexity of problem solving, the whole network is proposed to divide into many minimum simple sub-networks and every one is discretized respectively; third, based on the similarity between discretization and path planning, an ant colony algorithm is used to problem solving. Experimental results show that the proposed method could do well in discretization of continuous variables so as to improve the inference performance.