- 著者
-
Guoliang LI
Lining XING
Zhongshan ZHANG
Yingwu CHEN
- 出版者
- 一般社団法人 電子情報通信学会
- 雑誌
- IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (ISSN:09168508)
- 巻号頁・発行日
- vol.E100.A, no.7, pp.1541-1551, 2017-07-01 (Released:2017-07-01)
- 参考文献数
- 34
- 被引用文献数
-
4
Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.