An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach
Document Type
Journal article
Source Publication
IEEE Transactions on Evolutionary Computation
Publication Date
8-1-2004
Volume
8
Issue
4
First Page
378
Last Page
404
Abstract
Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the naïve Bayesian classifiers, and the tree-augmented naïve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.
DOI
10.1109/TEVC.2004.830334
Print ISSN
1089778X
E-ISSN
19410026
Publisher Statement
Copyright © 2004 IEEE
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., & Leung, K. S. (2004). An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach. IEEE Transactions on Evolutionary Computation, 8(4), 378-404. doi: 10.1109/TEVC.2004.830334