Title

Direct marketing modeling using evolutionary Bayesian network learning algorithm

Document Type

Book chapter

Source Publication

Marketing intelligent systems using soft computing : managerial and research applications

Publication Date

1-1-2010

First Page

273

Last Page

294

Publisher

Springer

Keywords

Direct Marketing Modeling, Data Mining, Bayesian Networks, Evolutionary Algorithms

Abstract

Direct marketing modeling identifies effective models for improving managerial decision making in marketing. This paper proposes a novel system for discovering models represented as Bayesian networks from incomplete databases in the presence of missing values. It combines an evolutionary algorithm with the traditional Expectation-Maximization(EM) algorithm to find better network structures in each iteration round. A data completing method is also presented for the convenience of learning and evaluating the candidate networks. The new system can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms and the efficiency problem in some existing evolutionary algorithms. We apply it to a real-world direct marketing modeling problem, and compare the performance of the discovered Bayesian networks with other models obtained by other methods. In the comparison, the Bayesian networks learned by our system outperform other models.

DOI

10.1007/978-3-642-15606-9_18

Publisher Statement

Copyright © Springer-Verlag Berlin Heidelberg 2010

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

Additional Information

ISBN of the source publication: 9783642156052

Full-text Version

Publisher’s Version

Recommended Citation

Wong, M. L. (2010). Direct marketing modeling using evolutionary Bayesian network learning algorithm. In J. Casillas & F. J. Martínez-López (Eds.), Marketing intelligent systems using soft computing: Managerial and research applications (pp.273-294). doi: 10.1007/978-3-642-15606-9_18