计算机应用与软件2024，Vol.41Issue(12)：261-267,313,8.DOI:10.3969/j.issn.1000-386x.2024.12.037
基于强化学习的贝叶斯网络模型生成方法研究
BAYESIAN NETWORK MODEL GENERATION METHOD BASED ON REINFORCEMENT LEARNING
摘要
Abstract
The network structure of the traditional Bayesian network needs to be determined in advance,and the reliability and accuracy of the model are low when used for prediction.Therefore,a Bayesian network model generation method based on reinforcement learning is proposed.The reinforcement learning was used to search for the optimal generalization residual score,and the Bayesian network was abstracted into a directed acyclic graph by constructing an adjacency matrix.For the completed Bayesian network,a Bayesian network structure optimization method based on causal direction judgment was proposed.The experimental results show that the method in this paper is superior to all kinds of traditional Bayesian network structure generation methods.关键词
贝叶斯网络/强化学习/并行集成/因果方向判断/结构生成与优化Key words
Bayesian network/Reinforcement learning/Parallel integration/Causal direction judgment/Structure generation and optimization分类
信息技术与安全科学引用本文复制引用
岑岗,郑泽宇,岑跃峰,王佳晨,吴思凡..基于强化学习的贝叶斯网络模型生成方法研究[J].计算机应用与软件,2024,41(12):261-267,313,8.基金项目
教育部人文社会科学研究一般规划基金项目(17YJA880004). （17YJA880004）
