Dynamic aspects in neural classification

When using neural networks for classification in an environment that is changing as time proceeds, methods for updating the parameters of the neural network should be considered in order to retain classification accuracy. Otherwise the neural network may lose performance due to structural changes. A classifier could be completely relearned from scratch at regular intervals. However, our experience from past credit scoring applications shows that users commonly prefer systems that change in as few cases as possible. Furthermore, this approach may be wasteful regarding the required computing time and that previously learned information will always be discarded. Therefore, we favor a methodology that attempts to detect changes and adapts a classifier only if inevitable. In this article, some methods for detecting and treating structural changes are applied to a credit scoring application. The results show that these methods may successfully be applied in a dynamic setting. Copyright © 1999 John Wiley & Sons, Ltd.