Improving the k-NCN classification rule through heuristic modifications

This paper presents an empirical investigation of the recently proposed k-Nearest Centroid Neighbours (k-NCN) classi®cation rule along with two heuristic modi®cations 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 signi®cantly better classi®cation rates than the k-Nearest Neighbours rule, basically due to the properties of the plain k-NCN technique.