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Multiple Classifier Fusion Using k-Nearest Localized Templates

  • Jun-Ki Min
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Classifier fusion Decision templates C-means clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jun-Ki Min
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer Science, Yonsei University, Biometrics Engineering Research Center, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749Korea

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