基于v-结构和邻居集的启发式贝叶斯网络结构学习方法  

Heuristic Bayesian network structure learning method based on v-structure and neighbor set

作　　者：徐苗 王慧玲 梁义 綦小龙 Xu Miao;Wang Huiling;Liang Yi;Qi Xiaolong(School of Cybersecurity&Information Technology,Yili Normal University,Yining Xinjiang 835000,China)

机构地区：[1]伊犁师范大学网络安全与信息技术学院,新疆伊宁835000

出　　处：《计算机应用研究》2022年第2期442-446,共5页Application Research of Computers

基　　金：新疆维吾尔自治区自然科学基金资助项目(2021D01C467);新疆维吾尔自治区高校科研项目(XJEDU2020Y036);伊犁师范大学博士科研启动项目(2020YSBS007);伊犁师范大学科研重点项目(2020YSZD004)。

摘　　要：针对K2算法存在的序依赖性问题,提出了能够从给定数据集中有效学习变量序的启发式算法(H-vnK2)。具体而言,基于PC算法学习的v-结构知识以节点块的形式快速准确修正部分父子节点顺序,获得部分节点的最优序;基于PC算法学习的邻居集知识以距离阈值启发式策略进一步从全局最优角度修正父子节点顺序,获得所有节点的最佳序。实验表明,在标准数据集Asia、Alarm网络上,所提算法显著优于对比算法,其中与性能最好的基于因果效应的方法相比,准确率平均提升了7%,增量最高能达到33.3%,可以学习到更准确的网络结构。Aiming at the order dependency problem of the K2 algorithm,this paper proposed a heuristic algorithm(H-vnK2)to learn the node order effectively from a given data set.Specifically,based on the v-structure knowledge learned by the PC algorithm,it corrected the order of some parent-child nodes in the form of node blocks quickly and accurately,and then obtained the optimal order of some nodes.Based on the neighbor set knowledge learned by the PC algorithm,with the distance threshold heuristic strategy,it further modified the order of the parent-child nodes from the global optimal point of view,and then obtained the best order of all nodes.Experiments show that the proposed algorithm is significantly better than the comparison algorithm on the Asia and Alarm standard data set.Among them,compared with the best-performing method based on causal effects,the proposed algorithm improves the accuracy rate by 7%on average,and the increment can reach up to 33.3%.It can learn a more accurate network structure.

关 键 词：变量序 K2算法 v-结构 邻居集 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]