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Published in:
2007 | OriginalPaper | Chapter
Multiple Classifier Fusion Using k-Nearest Localized Templates
Authors : Jun-Ki Min, Sung-Bae Cho
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Publisher: Springer Berlin Heidelberg
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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.