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
Language
English
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