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Rough set-based approach to feature selection in customer relationship management

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  • (Bill) Tseng, Tzu-Liang
  • Huang, Chun-Che

Abstract

In this paper, application of the rough set theory (RST) to feature selection in customer relationship management (CRM) is introduced. Compared to other methods, the RST approach has the advantage of combining both qualitative and quantitative information in the decision analysis, which is extremely important for CRM. To derive the decision rules from historical data for identifying features that contribute to CRM, both the mathematical formulation and the heuristic algorithm are developed in this paper. The proposed algorithm is comprised of both equal and unequal weight cases of the feature content with the limitation of the mathematical models. This algorithm is able to derive the rules and identify the most significant features simultaneously, which is unique and useful in solving CRM problems. A case study of a video game system purchase is validated by historical data, and the results showed the practical viability of the RST approach for predicting customer purchasing behavior. This paper forms the basis for solving many other similar problems that occur in the service industry.

Suggested Citation

  • (Bill) Tseng, Tzu-Liang & Huang, Chun-Che, 2007. "Rough set-based approach to feature selection in customer relationship management," Omega, Elsevier, vol. 35(4), pages 365-383, August.
    • Handle: RePEc:eee:jomega:v:35:y:2007:i:4:p:365-383
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    References listed on IDEAS

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    1. Lee, Seungkoo & Vachtsevanos, George, 2002. "An application of rough set theory to defect detection of automotive glass," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 225-231.
    2. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    3. Eric T. Anderson, 2002. "Sharing the Wealth: When Should Firms Treat Customers as Partners?," Management Science, INFORMS, vol. 48(8), pages 955-971, August.
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    Cited by:

    1. Panagopoulos, Orestis P. & Pappu, Vijay & Xanthopoulos, Petros & Pardalos, Panos M., 2016. "Constrained subspace classifier for high dimensional datasets," Omega, Elsevier, vol. 59(PA), pages 40-46.
    2. Salvatore Greco & Benedetto Matarazzo & Roman Slowinski & Stelios Zanakis, 2011. "Global investing risk: a case study of knowledge assessment via rough sets," Annals of Operations Research, Springer, vol. 185(1), pages 105-138, May.
    3. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    4. Chun-Che Huang & Tzu-Liang Tseng & Fuhua Jiang & Yu-Neng Fan & Chih-Hua Hsu, 2014. "Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes," Annals of Operations Research, Springer, vol. 216(1), pages 163-189, May.

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