A “soft” K-nearest neighbor voting scheme

✍ Scribed by H. B. Mitchell; P. A. Schaefer

• Publisher: John Wiley and Sons
• Year: 2001
• Tongue: English
• Weight: 75 KB
• Volume: 16
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.1018

✦ Synopsis

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.