基于条件独立性测试的贝叶斯网结构学习改进算法  

An Improved Bayesian Network Structure Learning Algorithm Based on the Conditional Independence Test

作　　者：赵波[1] 吴庆畅[1] 尹世堂[2] 范菁[1] 

机构地区：[1]云南民族大学云南省高校无线传感器网络重点实验室,云南昆明650031 [2]云南民族大学继续教育学院,云南昆明650031

出　　处：《云南民族大学学报（自然科学版）》2011年第5期402-405,共4页Journal of Yunnan Minzu University:Natural Sciences Edition

基　　金：国家自然科学基金(60963026)

摘　　要：针对基于条件独立性测试贝叶斯网结构学习算法在删除完全图边时的不足,提出加入对节点x和y的互信息测试的改进算法,不但能充分考虑到D-分离原理中存在的3种图型结构,使学习到的网络结构更接近于解,而且还从一定程度上减少了三角团的存在,从而也将低了确定边方向时出现环路的概率.并通过实验证明改进算法是有效、可行的.For solving the defects of Bayesian network structure learning algorithm based on the conditional independence test, the paper proposes an improved algorithm that adds the mutual information between node x and y. The algorithm takes into account adequately the three existing graphical structures in the theory of D - separate. The algorithm can reduce the triangulated clique and the probability of the cyclic route in a directed graph. The network structure produced by the algorithm is closer to solution. The experimental results show that the algorithm is effective and feasible.

关 键 词：贝叶斯网 D-分离 条件独立测试 机器学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]