基于MWST-CS-K2算法的贝叶斯网络结构学习  

Bayesian network structure learning algorithm based on MWST-CS-K2

作　　者：刘继[1,2] 熊月霞 李磊[1,2] Liu Ji;Xiong Yuexia;Li Lei(School of Statistics&Data Science,Xinjiang University of Finance&Economics,Urumqi 830012,China;Xinjiang Social&Economic Statistics&Big Data Application Research Center,Xinjiang University of Finance&Economics,Urumqi 830012,China)

机构地区：[1]新疆财经大学统计与数据科学学院,乌鲁木齐830012 [2]新疆财经大学新疆社会经济统计与大数据应用研究中心,乌鲁木齐830012

出　　处：《计算机应用研究》2023年第1期160-164,171,共6页Application Research of Computers

基　　金：国家自然社科基金资助项目(72164034,71762028);新疆财经大学研究生科研项目(XJUFE2022K10)。

摘　　要：针对K2算法依赖最大父节点数和节点顺序的不足,提出了一种改进的贝叶斯网络结构学习算法(MWST-CS-K2)。该算法先通过构造最大支撑树(MWST)得到最大父节点数;再利用变量间的关联度和更新系数对加边、减边和反转边进行规则设定,通过改进的布谷鸟算法对鸟巢位置进行寻优,应用广度优先搜索策略搜索遍历得到节点顺序;最后将最大父节点数和节点顺序作为K2算法的输入搜索得到最终网络。实验表明,所提出的MWST-CS-K2算法在标准的ASIA、SACHS和CHILD网络数据测试中的平均正确边比率分别达到了97.3%、87.7%和95.6%,学习效果优于其他对比算法,获得的网络结构和标准的网络结构最为相似。Aiming at the deficiency that K2 algorithm depends on the maximum number of parent nodes and node of order,this paper proposed an improved Bayesian network structure learning algorithm(MWST-CS-K2).The algorithm firstly obtained the maximum number of parent nodes by constructing the maximum support tree(MWST).Then it used the correlation degree and updated coefficient between variables to set rules for adding,subtracting and reversing edges.The paper used improved cuckoo algorithm to optimize the nest location,and applied the breadth first search strategy to search and traverse to get the node order.Finally,the paper used maximum number of parent nodes and the order of nodes as the input of K2 algorithm to get the final network.Experiments show that in the standard ASIA,SACHS and CHILD network data tests,the average correct edge ratio of MWST-CS-K2 algorithm reaches 97.3%,87.7%and 95.6%respectively.The learning effect is better than other corresponding comparison algorithms,and the network structure obtains the most similar to the standard network structure.

关 键 词：贝叶斯网络 布谷鸟算法 K2算法 最大支撑树 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]