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Abstract
En 中文
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. (c) 2004 Elsevier B.V. All rights reserved.
Keywords:
CRM
variable selection
sampling
ensemble
neural network
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IF:
6.8
Papers:
3.8K
Citations:
1.5W
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