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

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.