Supervised classification and tunnel vision

Abstract

In recent decades many highly sophisticated methods have been developed for supervised classification. These developments involve complex models requiring complicated iterative parameter estimation schemes, and can achieve unprecedented performance in terms of misclassification rate. However, in focusing efforts on the single performance criterion of misclassification rate, researchers have abstracted the problem beyond the bounds of practical usefulness, to the extent that the supposed performance improvements are irrelevant in comparison with other factors influencing performance. Examples of such factors are given. An illustration is provided of a new method which, for the particular problem of credit scoring, improves a relevant measure of classification performance while maintaining interpretability. Copyright © 2005 John Wiley & Sons, Ltd.