DOI:10.11991/yykj.202305024网络出版地址：https://link.cnki.net/urlid/23.1191.U.20231215.1052.002基于课程学习权重集成的贝叶斯结构学习算法研究刘凯越，周鋆国防科技大学信息系统工程重点实验室，湖南长沙410073摘要：从大量复杂的数据中学习贝叶斯网络(Bayesiannetwork,BN)一直是一个难点问题，本文借鉴课程学习的思想，引入了一种适合于BN中节点之间互相影响程度的测量，然后划分课程阶段，分阶段构造无向图骨架，并利用优化函数对骨架进行优化；通过集成策略，将各个集成学习结果所得到的课程权重进行集合，并通过边过滤来减少错误边的出现；最后，通过爬山搜索构建BN结构。实验结果表明，在4个标准数据集上，本文所提方法具有较高的精确度和稳定性。与多种传统贝叶斯结构学习(Bayesiannetworkstructurelearning,BNSL)方法相比，本文所提方法性能平均提高了37.18%。本文分析结果可为BNSL的增量学习过程进一步提供参考。关键词：贝叶斯网络；结构学习；课程学习；权重；边约束；权重互信息；集成学习；无向图骨架中图分类号：TP181文献标志码：A文章编号：1009−671X(2024)01−0001−09BayesiannetworkstructurelearningbasedoncurriculumlearningweightintegrationLIUKaiyue,ZHOUYunScienceandTechnologyonInformationSystemsandEngineeringLaboratory,NationalUniversityofDefenseTechnology,Changsha410073,ChinaAbstract:LearningBayesiannetwork(BN)fromalargenumberofcomplexdatahasalwaysbeenadifficultproblem.Basedontheideaofcourselearning,thispaperintroducesameasurementsuitableforthedegreeofmutualinfluencebetweennodesinBNstructure,thendividesthecoursestage,constructstheundirectedgraphskeletoninstages,andusestheoptimizationfunctiontooptimizetheskeleton.Throughtheintegrationstrategy,thecourseweightsobtainedfromeachintegratedlearningresultareaggregated,andtheerroredgesarereducedbyedgefiltering.Finally,theBNstructureisconstructedbyhill-climbingsearch.Theexperimentalresultsindicatethatthemethodproposedinthispaperexhibitshighprecisionandstabilityonfourstandarddatasets.Comparedwithvarioustraditionalbayesiannetworkstructurelearning(BNSL)methods,themethodproposedinthispapershowsanaverageperformanceimprovementof37.18%.Theanalyticalresultspresentedinthispapercanfurthe...
