Advances in neural information processing systems 2
Neural networks and the bias/variance dilemma
Neural Computation
Forward additive neural network models
Forward additive neural network models
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Consistency-based search in feature selection
Artificial Intelligence
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
Integrated feature architecture selection
IEEE Transactions on Neural Networks
Neural-network feature selector
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A comparison of nonlinear methods for predicting earnings surprises and returns
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
An efficient approach for building customer profiles from business data
Expert Systems with Applications: An International Journal
Using Data Mining for Wine Quality Assessment
DS '09 Proceedings of the 12th International Conference on Discovery Science
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
The data complexity index to construct an efficient cross-validation method
Decision Support Systems
Customer portfolio analysis using the SOM
International Journal of Business Information Systems
Two New Prediction-Driven Approaches to Discrete Choice Prediction
ACM Transactions on Management Information Systems (TMIS)
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This study shows how artificial neural networks can be used to model consumer choice. Our study focuses on two key issues in neural network modeling - model building and feature selection. Using the cross-validation approach, we address these two issues together and specifically examine the effectiveness of a backward feature selection algorithm for consumer situational choices of communication modes. Results indicate that the proposed heuristic for feature selection is robust with respect to validation sample variation. In fact, the feature selection approach produces the same best subset of features as the all-possible-subset approach.