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Nearest Neighbor Classification Using Cam Weighted Distance

  • Chang Yin Zhou
  • Yan Qiu Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

Nearest Neighbor (NN) classification assumes class conditional probabilities to be locally constant, and suffers from bias in high dimensions with a small sample set. In this paper, we propose a novel cam weighted distance to ameliorate the curse of dimensionality. Different from the existing neighbor-based methods, which only analyze a small space emanating from the query sample, the proposed nearest neighbor classification using cam weighted distance (CamNN) optimizes the distance measure based on the analysis of the inter-prototype relationships. Experiments show that CamNN significantly outperforms one nearest neighbor classification (1-NN) and k-nearest neighbor classification (k-NN) in most benchmarks, while its computational complexity is competitive with 1-NN classification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chang Yin Zhou
    • 1
  • Yan Qiu Chen
    • 1
  1. 1.Department of Computer Science and Engineering, School of Information Science and EngineeringFudan UniversityShanghaiP.R. China

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