COMBINING NEURAL NETWORKS AND STATISTICAL PREDICTIONS TO SOLVE THE CLASSIFICATION PROBLEM IN DISCRIMINANT-ANALYSIS

A number of recent studies have compared the performance of neural net works (NNs) to a variety of statistical techniques for the classificat ion problem in discriminant analysis. The empirical results of these c omparative studies indicate that while NNs often outperform the more t raditional statistical approaches to classification, this is not alway s the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particula r data set. We present a new approach to solving classification proble ms by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation.