南京邮电大学学报（自然科学版）2024，Vol.44Issue(6)：128-138,11.DOI:10.14132/j.cnki.1673-5439.2024.06.013
基于缩放框架的改进贝叶斯网络结构优化算法
An improved optimization algorithm for Bayesian network structure based on scaling framework
摘要
Abstract
Finding the optimal network structure is an NP-hard problem for Bayesian networks'probabilistic inference.In order to accurately model the causal relationships between nodes,this paper proposes a learning algorithm with an improved network structure based on a scaling framework.Firstly,the scaling framework is used for causal analysis to determine the strength of causal relationships between nodes through the slope matrix.This result is used as the basis for constructing the network search space,and the initial score of the network structure can be improved.Secondly,the coati optimization algorithm based on scoring methods is used to find the network structure with the highest score.Thus,the scoring search ability in Bayesian networks is enhanced.Finally,the structure with the highest score is processed by the add-arc,the subtract-arc and the steering-arc operations,and the optimal structure with the highest degree of fitting is found.Simulation experiments are conducted on standard networks with different complexities,and the results show that the proposed algorithm converges faster,can find the optimal structure in a shorter time,and has a higher score of structure learning and a higher convergence accuracy.These indicate that the algorithm has more advantages in accuracy and search efficiency.关键词
贝叶斯网络/结构学习/缩放框架/评分方法/浣熊优化算法Key words
Bayesian network/structure learning/scaling framework/scoring methods/coati optimization algorithm(COA)分类
信息技术与安全科学引用本文复制引用
祁煜翔,钱龙霞,王友国,黄海平..基于缩放框架的改进贝叶斯网络结构优化算法[J].南京邮电大学学报（自然科学版）,2024,44(6):128-138,11.基金项目
国家自然科学基金(42375016)、教育部人文社会科学研究规划基金(23YJAZH111)和中国气象局高影响天气专项重点开放实验室开放课题资助项目 （42375016）
