A "soft" K-Nearest Neighbor voting scheme

The K-Nearest Neighbor (K-NN) voting scheme is widely used in problems requiring pattern recognition or classification. In this voting scheme an unknown pattern is classified according to the classifications of its K nearest neighbors. If a majority of the K nearest neighbors have a given classification C*, then the unknown pattern is also given the classification C*. Although the scheme works well it is sensitive to the number of nearest neighbors, K, which is used. In this paper we describe a fuzzy K-NN voting scheme in which effectively the value of K varies automatically according to the local density of known patterns. We find that the new scheme consistently outperforms the traditional K-NN algorithm. (C) 2001 John Wiley & Sons, Inc.