Learning Bayesian network structures with discrete particle swarm optimization algorithm

被引：14
|
作者：
Xing-Chen, Heng [1 ]
Zheng, Qin [1 ]
Lei, Tian [1 ]
Li-Ping, Shao [1 ]
机构：
[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China
来源：
2007 IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTATIONAL INTELLIGENCE, VOLS 1 AND 2 | 2007年
关键词：
D O I：
10.1109/FOCI.2007.372146
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel structure learning algorithm of Bayesian networks (BNs) using particle swarm optimization (PSO) is proposed. For searching in structure spaces efficiently, a discrete PSO algorithm is designed in term of the characteristics of BNs. Firstly, fitness function is given to evaluate the structure of BN. Then, encoding and operations for PSO are designed to provide guarantee of convergence. Finally, experimental results show that this PSO based learning algorithm outperforms genetic algorithm based learning algorithm in convergence speed and quality of obtained structures.
引用
收藏
页码：47 / +
页数：2
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