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Multiple Classifier Fusion Using k-Nearest Localized Templates

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Intelligent Data Engineering and Automated Learning - IDEAL 2007 (IDEAL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4881))

Abstract

This paper presents a method for combining classifiers that uses k-nearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Biometrics Engineering Research Center, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Korea

    Jun-Ki Min & Sung-Bae Cho

Authors
  1. Jun-Ki Min
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  2. Sung-Bae Cho
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Editor information

Hujun Yin Peter Tino Emilio Corchado Will Byrne Xin Yao

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© 2007 Springer-Verlag Berlin Heidelberg

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Min, JK., Cho, SB. (2007). Multiple Classifier Fusion Using k-Nearest Localized Templates. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_46

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  • DOI: https://doi.org/10.1007/978-3-540-77226-2_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77225-5

  • Online ISBN: 978-3-540-77226-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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