Structure Learning Algorithm of DBN Based on Particle Swarm Optimization

摘要：

　　According to the characteristics dynamic Bayesian network structure,in which the build process,due to the transfer of network nodes is twice the number of variables to be solved,using the traditional method of Bayesian network construction slow efficiency,this article will apply PSO dynamic Bayesian network structure learning,and According to the characteristics Transferred network node in the network is divided into two parts,using stepwise build network has to choose sides proposed DBN structure learning algorithm based on particle swarm optimization.The last benchmark datasets large number of experiments show that the algorithm can improve the efficiency of dynamic Bayesian network structure learning.

关键词： dynamic bayesian networks structure learning transferred network particle swarm optimization Data Mining

作者: Yuansheng Lou Yuchao Dong Huanhuan Ao

作者单位: College of Computer and Information Hohai University Nanjing,China

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 102-105

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）