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Adaptive soft k-nearest-neighbour classifiers

✍ Scribed by Sergio Bermejo; Joan Cabestany

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
124 KB
Volume
33
Category
Article
ISSN
0031-3203

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✦ Synopsis

A novel classi"er is introduced to overcome the limitations of the k-NN classi"cation systems. It estimates the posterior class probabilities using a local Parzen window estimation with the k-nearest-neighbour prototypes (in the Euclidean sense) to the pattern to classify. A learning algorithm is also presented to reduce the number of data points to store. Experimental results in two hand-written classi"cation problems demonstrate the potential of the proposed classi"cation system.

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It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours ar