Information Processing Letters
Competitive learning algorithms for vector quantization
Neural Networks
Machine Learning
Estimating campaign benefits and modeling lift
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive modeling in automotive direct marketing: tools, experiences and open issues
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying prospective customers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of prediction models for marketing campaigns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Desktop Database Marketing
EROS: Ensemble rough subspaces
Pattern Recognition
Modeling consumer situational choice of long distance communication with neural networks
Decision Support Systems
A maximum entropy approach to feature selection in knowledge-based authentication
Decision Support Systems
Expert Systems with Applications: An International Journal
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
A novel decision rules approach for customer relationship management of the airline market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
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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.