Multiple classifier fusion using k-nearest localized templates

Authors:
Jun-Ki Min;Sung-Bae Cho
Affiliations:
Department of Computer Science, Yonsei University, Biometrics Engineering Research Center, Seoul, Korea;Department of Computer Science, Yonsei University, Biometrics Engineering Research Center, Seoul, Korea
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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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.