Weighted K-Nearest Neighbor Revisited

Bicego, M.; Loog, M.

Weighted K-Nearest Neighbor Revisited

Conference paper (2016)

Authors

M. Bicego University of Verona

M. Loog Pattern Recognition and Bioinformatics -

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Testing Pattern recognition Training Diversity reception Electronic mail Degradation Terminology

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http://resolver.tudelft.nl/uuid:e5938eb4-7a82-4cde-ab3c-9ef14e35fb60

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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance.