A simulated annealing approach to construct optimized prototypes for nearest-neighbor classification

A new method of optimizing prototypes for a nearest neighbor classifier is proposed based on a four-layer network architecture. A new error function is defined for updating prototypes. The physical meaning of the updating rule and the relationship between the proposed method and LVQ2 are also presented. The main characteristic of the present method is consistent criteria for updating prototypes and for using the trained prototypes to build a nearest neighbor classifier. Experimental results indicate that the present method is effective compared with LVQ2.