Title

Mining Bayesian networks from direct marketing databases with missing values

Document Type

Book chapter

Source Publication

Intelligent and evolutionary systems

Publication Date

1-1-2009

First Page

13

Last Page

35

Publisher

Springer-Verlag

Abstract

Discovering knowledge from huge databases with missing values is a challenging problem in Data Mining. In this paper, a novel hybrid algorithm for learning knowledge represented in Bayesian Networks is discussed. The new algorithm combines an evolutionary algorithm with the Expectation-Maximization (EM) algorithm to 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 network structures illustrate that our system outperforms some state-of-the-art algorithms. We also apply our system to a direct marketing problem, and compare the performance of the discovered Bayesian networks with the response models obtained by other algorithms. In the comparison, the Bayesian networks learned by our system outperform others.

DOI

10.1007/978-3-540-95978-6_2

Print ISSN

1860949X

E-ISSN

18609503

Publisher Statement

Copyright © Springer-Verlag Berlin Heidelberg 2009

Access to external full text or publisher's version may require subscription.

Additional Information

ISBN of the source publication: 9783540959779

Full-text Version

Publisher’s Version

Recommended Citation

Guo, Y. Y., & Wong, M. L. (2009). Mining Bayesian networks from direct marketing databases with missing values. In M. Gen, O. Katai, B. McKay, A. Namatame, R. A. Sarker & B.-T. Zhang (Eds.), Intelligent and evolutionary systems (pp.13-35). doi: 10.1007/978-3-540-95978-6_2