Toward a successful CRM: variable selection, sampling, and ensemble
Toward a successful CRM: variable selection, sampling, and ensemble
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DOI：
10.1016/j.dss.2004.09.008
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
YongSeog Kim
中科院分区：
文献类型：
--
作者：
YongSeog Kim
本文研究了变量选择和类分布对特定logit回归性能的影响（即，原始分类器系统）和人工神经网络（ANN;相对更复杂的分类器系统）在客户关系管理（CRM）设置中的实现。最后，通过组合多个分类器的预测来构建集成模型。本文表明，人工神经网络集成与变量选择显示出最稳定的性能在各种类分布。
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.