Improving nearest neighbor rule with a simple adaptive distance measure

Authors:
Jigang Wang;Predrag Neskovic;Leon N. Cooper
Affiliations:
Department of Physics, The Institute for Brain and Neural Systems, Brown University, P.O. Box 1843, Providence, RI 02912, USA;Department of Physics, The Institute for Brain and Neural Systems, Brown University, P.O. Box 1843, Providence, RI 02912, USA;Department of Physics, The Institute for Brain and Neural Systems, Brown University, P.O. Box 1843, Providence, RI 02912, USA
Venue:
Pattern Recognition Letters
Year:
2007

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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.