Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
552811 | Decision Support Systems | 2006 | 12 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
YongSeog Kim,