计算机工程2017，Vol.43Issue(5)：317-321,5.DOI:10.3969/j.issn.1000-3428.2017.05.052
基于节点排序的贝叶斯网络结构学习算法
Bayesian Network Structure Learning Algorithm Based on Node Ordering
姚洁 1朱响斌 1宋新方 2李广龙 3邱慧玲1
作者信息
- 1. 浙江师范大学数理与信息工程学院,浙江金华321004
- 2. 横店集团东磁股份有限公司,浙江东阳321118
- 3. 山东省曹县第一中学,山东曹县274400
- 折叠
摘要
Abstract
Due to the problem that K2 algorithm requires node ordering in learning Bayesian network structure,this paper proposes a hybrid Bayesian network structure learning algorithm.In the situation of a given data set,it uses Maxmin Parents and Children(MMPC) algorithm to obtain the initial network structure and utilizes the way of Breadth First Search (BFS) to search the initial network structure.It startly searchs from the node whose in-degree is zero and visits in turn the adjacent points in the figure according to level,thereby it can gain the node order and make it as the initial node order of K2 algorithm.Then,it uses K2 algorithm to search the network space to find out the global optimal solution.The experimental results show that compared with K2 algorithm and Restricted Particle Swarm Optimization (RPSO) algorithm,the new algorithm has lower probability of multi-edge,lack-edge and reverse-edge under the same sample data set.It can learn more accurate Bayesian network with faster convergence speed and higher precision.关键词
贝叶斯网络/结构学习/MMPC算法/K2算法/广度优先搜索Key words
Bayesian network/structure learning/Max-min Parents and Children (MMPC) algorithm/K2 algorithm/Breadth First Search(BFS)分类
信息技术与安全科学引用本文复制引用
姚洁,朱响斌,宋新方,李广龙,邱慧玲..基于节点排序的贝叶斯网络结构学习算法[J].计算机工程,2017,43(5):317-321,5.
