Comparison between two prototype representation schemes for a nearest neighbor classifier

The paper deals with the problem of finding good prototypes for a condensed nearest neighbor classified in a recognition system. A comparison study is done between two prototype representation schemes. The prototype search is done by a genetic algorithm which is able to generate novel prototypes (i.e. prototypes which are not among the training samples). It is shown that the generalized representation scheme is more powerful, giving significantly larger normalized interclass distances. It is also shown that both representation schemes with generic algorithm give significantly better prototypes than a direct prototype selection algorithm, which can select only among the training samples.