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Kernel Difference-Weighted k-Nearest Neighbors Classification

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 4682))

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Abstract

Nearest Neighbor (NN) rule is one of the simplest and most important methods in pattern recognition. In this paper, we propose a kernel difference-weightedk-nearest neighbor method (KDF-WKNN) for pattern classification. The proposed method defines the weighted KNN rule as a constrained optimization problem, and then we propose an efficient solution to compute the weights of different nearest neighbors. Unlike distance-weighted KNN which assigns different weights to the nearest neighbors according to the distance to the unclassified sample, KDF-WKNN weights the nearest neighbors by using both the norm and correlation of the differences between the unclassified sample and its nearest neighbors. Our experimental results indicate that KDF-WKNN is better than the original KNN and distance-weighted KNN, and is comparable to some state-of-the-art methods in terms of classification accuracy.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, 150001 Harbin, China

    Wangmeng Zuo, Kuanquan Wang & Hongzhi Zhang

  2. Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong

    David Zhang

Authors
  1. Wangmeng Zuo

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  2. Kuanquan Wang

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  3. Hongzhi Zhang

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  4. David Zhang

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Editor information

De-Shuang Huang Laurent Heutte Marco Loog

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© 2007 Springer-Verlag Berlin Heidelberg

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Zuo, W., Wang, K., Zhang, H., Zhang, D. (2007). Kernel Difference-Weighted k-Nearest Neighbors Classification. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_89

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