Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Document Type
Journal article
Source Publication
Decision Support Systems
Publication Date
5-1-2008
Volume
45
Issue
2
First Page
368
Last Page
383
Publisher
Elsevier BV
Keywords
Data mining, machine learning, Bayesian networks, evolutionary algorithms
Abstract
This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation Maximization (EM) algorithm. A data completing procedure is presented for learning and evaluating the candidate networks. Moreover, a strategy is introduced to obtain better initial networks to facilitate the method. The new method can also overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the databases generated from several benchmark networks illustrate that the new method has better performance than some state-of-the-art algorithms. We also apply the method to a data mining problem and compare the performance of the discovered Bayesian networks with the models generated by other learning algorithms. The results demonstrate that our method outperforms other algorithms.
DOI
10.1016/j.dss.2008.01.002
Print ISSN
01679236
E-ISSN
18735797
Publisher Statement
Copyright © 2008 Elsevier B.V.
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Wong, M. L., & Guo, Y. Y. (2008). Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm. Decision Support Systems, 45(2), 368-383. doi: 10.1016/j.dss.2008.01.002