Exploiting Randomness for Feature Selection in Multinomial Logit: a CRM Cross-Sell Application
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.
|Date of creation:||May 2006|
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- 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.
- 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.
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