Title

A hybrid approach to discover Bayesian networks from databases using evolutionary programming

Document Type

Conference paper

Source Publication

Proceedings - IEEE International Conference on Data Mining, ICDM

Publication Date

1-1-2002

First Page

498

Last Page

505

Abstract

This paper describes a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. 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. A new operator is introduced to further enhance the search efficiency. We conduct a number of experiments and compare the hybrid algorithm with our previous algorithm, MDLEP [18], which uses EP for network learning. The empirical results illustrate that the new approach has better performance. We apply the approach to a data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with the models generated by other methods. In the comparison, the induced Bayesian networks produced by the new algorithm outperform the other models.

DOI

10.1109/ICDM.2002.1183994

Print ISSN

15504786

Publisher Statement

Copyright © 2002 IEEE. Access to external full text or publisher's version may require subscription.

Additional Information

Paper presented at the 2nd IEEE International Conference on Data Mining, Dec 09-12, 2002, Maebashi City, Japan.

ISBN of the source publication: 9780769517544

Full-text Version

Publisher’s Version

Language

English

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