Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm
Document Type
Conference paper
Source Publication
Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006
Publication Date
1-1-2006
First Page
1146
Last Page
1150
Publisher
IEEE Computer Society
Abstract
This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.
DOI
10.1109/ICDM.2006.56
Print ISSN
15504786
Publisher Statement
Copyright © 2006 IEEE. Access to external full text or publisher's version may require subscription.
Additional Information
Paper presented at the 6th International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong.
ISBN of the source publication: 9780769527017
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Wong, M. L., & Guo, Y. Y. (2006). Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm. In Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006 (pp. 1146-1150). Los Alamitos: IEEE Computer Society. doi: 10.1109/ICDM.2006.56