自动化学报2009，Vol.35Issue(3)：281-288,8.DOI:10.3724/SP.J.1004.2009.00281
一种基于独立性测试和蚁群优化的贝叶斯网学习算法
A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization
摘要
Abstract
To solve the drawbacks of the ant colony optimization for learning Bayesian networks (ACO-B), this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization (I-ACO-B). First, the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions, so that many unnecessary searches of ants can be avoided. And then, by combining the global score increase of a solution and local mutual information between nodes, a new heuristic function with better heuristic ability is given to induct the process of stochastic searches. The experimental results on the benchmark data sets show that the new algorithm is effective and efficient in large scale databases, and greatly enhances convergence speed compared to the original algorithm.关键词
Uncertainty modeling/Bayesian network structure learning/ant colony optimization (ACO)/conditional independence testKey words
Uncertainty modeling/Bayesian network structure learning/ant colony optimization (ACO)/conditional independence test分类
信息技术与安全科学引用本文复制引用
冀俊忠,张鸿勋,胡仁兵,刘椿年..一种基于独立性测试和蚁群优化的贝叶斯网学习算法[J].自动化学报,2009,35(3):281-288,8.基金项目
Supported by National Natural Science Foundation of China 60496322), Natural Science Foundation of Beijing (4083034), and cientific Research Common Program of Beijing Municipal Com-mission of Education (KM200610005020) （4083034）
