一种基于独立性测试和蚁群优化的贝叶斯网学习算法(英文)

• 作 者: 冀俊忠 张鸿勋 胡仁兵 刘椿年
• 机 构: Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology,College of Comput
• 出 处: 自动化学报
• 发表日期: 2009年03期
• 关键词: heuristic independence 蚁群优化 Bayesian colony mutual stochastic 贝叶斯 benchmark unne

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.

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 effectively effectively the space of candidate solutions, so that many random 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.