Title

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

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

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