An Approach for Prototype Generation based on Similarity Relations for Problems of Classification

Abstract

In this paper, a new method for solving classification problems based on prototypes is proposed. When using similarity relations for granulation of a universe, similarity classes are generated, and a prototype is constructed for each similarity class. Experimental results show that the proposed method has higher classification accuracy and a satisfactory reduction coefficient compared to other well-known methods, proving to be statistically superior in terms of classification’s precision.