Dynamic generation of prototypes with self-organizing feature maps for classifier design

We propose a new scheme for designing a nearest-prototype classifier using Kohonen's self-organizing feature map (SOFM). The net starts with the minim um number of prototypes which is equal to the number of classes. Then on th e basis of the classification performance, new prototypes are generated dyn amically. The algorithm merges similar prototypes and deletes less signific ant prototypes. If prototypes are deleted or new prototypes appear then the y are fine tuned using Kohonen's SOFM algorithm with the winner-only update strategy. This adaptation continues until the system satisfies a terminati on condition. The classifier has been tested with several well-known data s ets and the results obtained are quite satisfactory. (C) 2000 Pattern Recog nition Society. Published by Elsevier Science Ltd. All rights reserved.