基于链模型和粒子群的贝叶斯网结构学习算法

Bayesian Network Structure Learning Algorithm Based on Chain-model and Particle Swarm

为提高学习贝叶斯网络结构的效率,提出一种基于链模型和粒子群的学习算法。利用包含贝叶斯网节点间因果关系信息的规则链模型来衡量拓扑序列的优劣,提高搜索的拓扑序列的质量,为粒子位置可选择的优化算法加上动态权重系数,平衡全局搜索和局部搜索,提高算法的搜索能力。实验结果表明,与I-ACO-B算法相比,该算法不仅能获得更好的解,且收敛速度也有一定的提高。

To improve the efficiency of Bayesian Network（BN） structure learning,this paper developes a novel algorithm based on chain-model and particle swarm.It measures the quality of topological orders with a regular chain-model which contains information of causality between nodes,so that the quality of the searched topological orders is improved.And then the position-selectable updating PSO is optimized with dynamical weighting coefficient,which balances global search and local search and improves search capability.Experimental results show that the novel algorithm can get a better solution,and convergence rate is enhanced with a comparison to the I-ACO-B algorithm.