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The Impact Of Feature Selection: A Data‐Mining Application In Direct Marketing

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  • Ding‐Wen Tan
  • William Yeoh
  • Yee Ling Boo
  • Soung‐Yue Liew

Abstract

The capability of identifying customers who are more likely to respond to a product is an important issue in direct marketing. This paper investigates the impact of feature selection on predictive models which predict reordering demand of small and medium‐sized enterprise customers in a large online job‐advertising company. Three well‐known feature subset selection techniques in data mining, namely correlation‐based feature selection (CFS), subset consistency (SC) and symmetrical uncertainty (SU), are applied in this study. The results show that the predictive models using SU outperform those without feature selection and those with the CFS and SC feature subset evaluators. This study has examined and demonstrated the significance of applying the feature‐selection approach to enhance the accuracy of predictive modelling in a direct‐marketing context. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Ding‐Wen Tan & William Yeoh & Yee Ling Boo & Soung‐Yue Liew, 2013. "The Impact Of Feature Selection: A Data‐Mining Application In Direct Marketing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(1), pages 23-38, January.
    • Handle: RePEc:wly:isacfm:v:20:y:2013:i:1:p:23-38
    DOI: 10.1002/isaf.1335
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    References listed on IDEAS

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    1. Sikora, Riyaz & Piramuthu, Selwyn, 2007. "Framework for efficient feature selection in genetic algorithm based data mining," European Journal of Operational Research, Elsevier, vol. 180(2), pages 723-737, July.
    2. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    3. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    4. Piramuthu, Selwyn, 2004. "Evaluating feature selection methods for learning in data mining applications," European Journal of Operational Research, Elsevier, vol. 156(2), pages 483-494, July.
    5. Meiri, Ronen & Zahavi, Jacob, 2006. "Using simulated annealing to optimize the feature selection problem in marketing applications," European Journal of Operational Research, Elsevier, vol. 171(3), pages 842-858, June.
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