Design of a nearest-prototype classifier with dynamically generated prototypes using self-organizing feature maps

Abstract

Proposes a new scheme for designing a nearest-prototype classifier. The system starts with the minimum number of prototypes, equal to the number of classes. Kohonen's self-organizing feature map (SOFM) algorithm is used to obtain this initial set of prototypes. Then, on the basis of the classification performance, new prototypes are generated dynamically, similar prototypes are merged, and prototypes with less significance are deleted, leading to better performance. If prototypes are deleted or new prototypes appear, then they are retrained using Kohonen's SOFM algorithm with the winner-only update scheme. This adaptation continues until the system satisfies a termination condition. The classifier has been tested with several well-known data sets. The results obtained are quite satisfactory.