首页|基于课程学习权重集成的贝叶斯结构学习算法研究

基于课程学习权重集成的贝叶斯结构学习算法研究

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从大量复杂的数据中学习贝叶斯网络(Bayesian network,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(Bayesian network structure learning,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.
Bayesian network structure learning based on curriculum learning weight integration
Learning Bayesian network(BN)from a large number of complex data has always been a difficult problem.Based on the idea of course learning,this paper introduces a measurement suitable for the degree of mutual influence between nodes in BN structure,then divides the course stage,constructs the undirected graph skeleton in stages,and uses the optimization function to optimize the skeleton.Through the integration strategy,the course weights obtained from each integrated learning result are aggregated,and the error edges are reduced by edge filtering.Finally,the BN structure is constructed by hill-climbing search.The experimental results indicate that the method proposed in this paper exhibits high precision and stability on four standard datasets.Compared with various traditional bayesian network structure learning(BNSL)methods,the method proposed in this paper shows an average performance improvement of 37.18％.The analytical results presented in this paper can further provide insights into the incremental learning process of BNSL.

Bayesian networkstructure learningcurriculum learningweightedge constraintweighted mutual informationensemble learningundirected graph skeleton

刘凯越、周鋆

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国防科技大学 信息系统工程重点实验室,湖南 长沙 410073

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

国家自然科学基金项目湖南省科技创新计划长沙市优秀青年创新者培训班项目

622762622021RC3076KQ2009009

2024

10.11991/yykj.202305024

应用科技
哈尔滨工程大学

应用科技

CSTPCD
影响因子：0.693
ISSN：1009-671X
年,卷(期)：2024.51(1)
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