Toward a successful CRM: variable selection, sampling, and ensemble

Authors:
YongSeog Kim
Affiliations:
Business Information Systems, Utah State University, UMC 3515 Old Main Hill, Logan, UT 84322, USA
Venue:
Decision Support Systems
Year:
2006

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Abstract

This paper studies the effects of variable selection and class distribution on the performance of specific logit regression (i.e., a primitive classier system) and artificial neural network (ANN; a relatively more sophisticated classifier system) implementations in a customer relationship management (CRM) setting. Finally, ensemble models are constructed by combining the predictions of multiple classiers. This paper shows that ANN ensembles with variable selection show the most stable performance over various class distributions.