A cluster-based merging strategy for nearest prototype classifiers

A generalized prototype-based learning scheme founded on hierarchical clustering is proposed. The basic idea is to obtain a condensed nearest neighbor classification rule by replacing a group of prototypes by a representative while approximately keeping their original classification power. The algorithm improves and generalizes previous works by explicitly introducing the concept of cluster and cluster consistency. The proposed scheme also permits a very efficient implementation based on geometric cluster properties. Empirical results demonstrate the merits of the proposed algorithm taking into account the size of the condensed sets of prototypes, the accuracy of the corresponding condensed 1-NN classification rule and the computation time