Improving nearest neighbor rule with a simple adaptive distance measure

Authors:
Jigang Wang;Predrag Neskovic;Leon N. Cooper
Affiliations:
Institute for Brain and Neural Systems, Department of Physics, Brown University, Providence, RI;Institute for Brain and Neural Systems, Department of Physics, Brown University, Providence, RI;Institute for Brain and Neural Systems, Department of Physics, Brown University, Providence, RI
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 2
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Abstract

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. However, it faces serious challenges when patterns of different classes overlap in some regions in the feature space. In the past, many researchers developed various adaptive or discriminant metrics to improve its performance. In this paper, we demonstrate that an extremely simple adaptive distance measure significantly improves the performance of the k-nearest neighbor rule.