Prototype search for a nearest neighbor classifier by a genetic algorithm

Abstract

Deals with the problem of finding good prototypes for a condensed nearest-neighbor classifier for a character recognition system based on directional codes. A prototype search by a genetic algorithm which is capable of creating new prototypes is compared against a direct prototype selection algorithm. It is shown, in a leave-one-out experiment, that the prototypes found by the genetic algorithm give significantly smaller intra-class distances to class members and significantly larger normalized inter-class distances.