A principled approach for building and evaluating neural network classification models

In this paper, we propose a principled approach to building and evaluating neural network classification models for decision support system implementations. First, the usefulness of neural networks for use with e-commerce data and for Bayesiam classification is discussed. Next, the theory concerning model accuracy and generalization is presented. Then, the principled approach, which is developed with consideration of these issues, is described. Through an illustrative problem, it is seen that when the problem complexity is considered, the classification performance of the neural networks can be much better than what is observed. Furthermore, it is seen that model order selection processes based upon a single dataset can lead to an incorrect conclusion concerning the best model, which impacts model error and utility.