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|
|Date of revision:|
|Contact details of provider:|| Postal: Hoveniersberg 4, B-9000 Gent|
Phone: ++ 32 (0) 9 264 34 61
Fax: ++ 32 (0) 9 264 35 92
Web page: http://www.ugent.be/eb
More information through EDIRC
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.:
- 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.
- Johnson, Michael D, 1984. " Consumer Choice Strategies for Comparing Noncomparable Alternatives," Journal of Consumer Research, Oxford University Press, vol. 11(3), pages 741-53, December.
When requesting a correction, please mention this item's handle: RePEc:rug:rugwps:06/390. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nathalie Verhaeghe)
If references are entirely missing, you can add them using this form.