Exploiting Randomness for Feature Selection in Multinomial Logit: a CRM Cross-Sell Application
AbstractData 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.
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Bibliographic InfoPaper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 06/390.
Length: 15 pages
Date of creation: May 2006
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-10-28 (All new papers)
- NEP-DCM-2006-10-28 (Discrete Choice Models)
- NEP-ECM-2006-10-28 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Johnson, Michael D, 1984. " Consumer Choice Strategies for Comparing Noncomparable Alternatives," Journal of Consumer Research, University of Chicago Press, vol. 11(3), pages 741-53, December.
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