一种增强贝叶斯网络结构学习的自动变量序调整算法  

An automatic variable order adjustment algorithm to enhance the structure learning of Bayesian networks

作　　者：许国强[1] 余长州 王林[1] 周春蕾[1] 高阳[2] Xu Guoqiang;Yu Changzhou;Wang Lin;Zhou Chunlei;Gao Yang(Jiangsu Frontier Electric Technology Co.Ltd.,Nanjing,211102,China;Department of Computer Science and Technology,Nanjing University,Nanjing,210023,China)

机构地区：[1]江苏方天电力技术有限公司,南京211102 [2]南京大学计算机科学与技术系,南京210023

出　　处：《南京大学学报（自然科学版）》2021年第2期255-261,共7页Journal of Nanjing University（Natural Science）

基　　金：江苏方天电力技术有限公司科技项目(KJ201919)。

摘　　要：现有的混合结构学习算法受制于变量的邻居集,导致混合结构学习算法在约束学习阶段,若变量的邻居集没有包含真实结构的节点,该节点将再也不会被考虑.为改进这一问题,通过探索贝叶斯网络结构与节点影响度间存在的可能性关系,设计基于节点影响度的变量序调整方法并将调整后的变量序应用于网络结构学习.调整后的变量序在减少搜索空间的同时,也改善了传统约束空间过于依赖变量邻居集的问题,进而提升网络结构的学习质量.实验结果表明,该算法能有效地提升现有混合结构学习算法的精度,同时也验证了从节点影响度的角度去探索贝叶斯网络结构图的可行性.The existing hybrid structure learning algorithm is subject to the neighbor set of variables,which results in the hybrid structure learning algorithm in the constraint learning stage.If the neighbor set of variables does not contain nodes of real structure,the nodes will not be considered.To improve this problem,this paper explores the Bayesian network structure and the possibility of relation between node effect degree,designs a variable order adjustment method based on node effect degree,and applies the adjusted variable order to network structure learning.The adjusted variable order not only reduces the search space,but also improves the problem that the traditional constraint space relies too much on the neighbor set of variables,thus improving the learning quality of the network structure.Experimental results show that the proposed algorithm can effectively improve the accuracy of existing hybrid structure learning algorithms,and also verify the feasibility of exploring Bayesian network structure diagram from the point of node effect degree.

关 键 词：贝叶斯网络结构 节点影响度 混合结构学习算法 变量序 约束空间 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]