 Rough set-based approach to feature selection in customer relationship management(Bill) Tseng, Tzu-Liang and Chun-Che Huang Omega, 2007, vol. 35, issue 4, 365-383 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. Keywords: Rough; set; theory; Feature; selection; Customer; relationship; management; Mathematical; programming; Heuristic; algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:35:y:2007:i:4:p:365-383 Ordering information: This journal article can be ordered from Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
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