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

摘要

We propose a new scheme for designing a nearest-prototype classifier using Kohonen's self-organizing feature map (SOFM). The net starts with the minimum number of prototypes which is equal to the number of classes. Then on the basis of the classification performance, new prototypes are generated dynamically. The algorithm merges similar prototypes and deletes less significant prototypes. If prototypes are deleted or new prototypes appear then they are fine tuned using Kohonen's SOFM algorithm with the winner-only update strategy. This adaptation continues until the system satisfies a termination condition. The classifier has been tested with several well-known data sets and the results obtained are quite satisfactory.