Improving the k-NCN classification rule through heuristic modifications

This paper presents an empirical investigation of the recently proposed k-N earest Centroid Neighbours (k-NCN) classification rule along with two heuri stic modifications of it. These alternatives make use of both proximity and geometrical distribution of the prototypes in the training set in order to estimate the class label of a given sample. The experimental results show that both alternatives give significantly better classification rates than the k-Nearest Neighbours rule, basically due to the properties of the plain k-NCN technique. (C) 1998 Published by Elsevier Science B.V. All rights re served.