计算机工程2017，Vol.43Issue(8)：225-230,6.DOI:10.3969/j.issn.1000-3428.2017.08.038
基于最大信息系数的贝叶斯网络结构学习算法
Bayesian Network Structure Learning Algorithm Based on Maximal Information Coefficient
摘要
Abstract
An improved Bayesian network structure learning algorithm is proposed by introducing Maximal Information Coefficient(MIC).Under the conditions of a given data set,MIC is used to measure dependency between the variables.An initial Bayesian network is constructed according to the screening and correlation factor.It is combined with the greedy algorithm to locally modify the initial network,integrat local optimal solution to form the global optimal solution,and generate the final network structure.Experimental results on Asia and Car benchmark networks show that,compared with BN structure learning algorithm based on traditional Greedy algorithm,random K2 algorithm,the algorithm is able to get the network structure which is close to that of the benchmark network and has higher mean of the right side and classification accuracy.关键词
贝叶斯网络/结构学习/最大信息系数/关联度/贪婪算法Key words
Bayesian Network(BN)/structure learning/Maximal Information Coefficient(MIC)/relevancy/greedy algorithm分类
信息技术与安全科学引用本文复制引用
曾千千,曾安,潘丹,杨海东,邓杰航..基于最大信息系数的贝叶斯网络结构学习算法[J].计算机工程,2017,43(8):225-230,6.基金项目
国家自然科学基金(61300107) （61300107）
广东省自然科学基金(S2012010010212). （S2012010010212）
