Modeling consumer situational choice of long distance communication with neural networks
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.
neural network consumer choices model building feature selection prediction risk