Direct marketing modeling using evolutionary Bayesian network learning algorithm

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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.
Original languageEnglish
Title of host publicationMarketing intelligent systems using soft computing : managerial and research applications
PublisherSpringer
Pages273-294
Number of pages22
ISBN (Print)9783642156052
DOIs
Publication statusPublished - 1 Jan 2010

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Keywords

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

Cite this

WONG, M. L. (2010). Direct marketing modeling using evolutionary Bayesian network learning algorithm. In Marketing intelligent systems using soft computing : managerial and research applications (pp. 273-294). Springer. https://doi.org/10.1007/978-3-642-15606-9_18