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Exploiting Randomness for Feature Selection in Multinomial Logit: a CRM Cross-Sell Application

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  • A. PRINZIE
  • D. VAN DEN POEL

Abstract

Data mining applications addressing classification problems must master two key tasks: feature selection and model selection. This paper proposes a random feature selection procedure integrated within the multinomial logit (MNL) classifier to perform both tasks simultaneously. We assess the potential of the random feature selection procedure (exploiting randomness) as compared to an expert feature selection method (exploiting domain-knowledge) on a CRM cross-sell application. The results show great promise as the predictive accuracy of the integrated random feature selection in the MNL algorithm is substantially higher than that of the expert feature selection method.

Suggested Citation

  • A. Prinzie & D. Van Den Poel, 2006. "Exploiting Randomness for Feature Selection in Multinomial Logit: a CRM Cross-Sell Application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/390, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:06/390
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    File URL: http://wps-feb.ugent.be/Papers/wp_06_390.pdf
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    References listed on IDEAS

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    1. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    2. Johnson, Michael D, 1984. "Consumer Choice Strategies for Comparing Noncomparable Alternatives," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(3), pages 741-753, December.
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    Cited by:

    1. Iva Salov & Aleksandra Krajnovic & Ante Panjkota, 2017. "Relation between Data Mining and Business Fields in the Four Dimensional CRM Model," MIC 2017: Managing the Global Economy; Proceedings of the Joint International Conference, Monastier di Treviso, Italy, 24–27 May 2017,, University of Primorska Press.

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