A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data
Document Type
Conference paper
Source Publication
Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006
Publication Date
1-1-2006
First Page
916
Last Page
923
Publisher
Institute of Electrical and Electronics Engineers
Abstract
Existing Structural Expectation-Maximization (EM) algorithms for learning Bayesian networks from incomplete data usually adopt the greedy hill climbing search method, which may make the algorithms find sub-optimal solutions. In this paper, we present a new Structural EM algorithm which employs a hybrid evolutionary algorithm as the search method. The experimental results on the data sets generated from several benchmark networks illustrate that our algorithm outperforms some state-of-the-art learning algorithms.
DOI
10.1109/CEC.2006.1688409
Publisher Statement
Copyright © 2006 IEEE. Access to external full text or publisher's version may require subscription.
Additional Information
Paper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.
ISBN of the source publication: 9780780394872
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Guo, Y.-Y., Wong, M.-L., & Cai, Z.-H. (2006). A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data. In Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 916-923). Piscataway: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2006.1688409