• Title/Summary/Keyword: C-Meant 클러스터링

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Multiple Classifier Fusion Method based on k-Nearest Templates (k-최근접 템플릿기반 다중 분류기 결합방법)

  • Min, Jun-Ki;Cho, Sung-Bae
    • Journal of KIISE:Computing Practices and Letters
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    • v.14 no.4
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    • pp.451-455
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    • 2008
  • In this paper, the k-nearest templates method is proposed to combine multiple classifiers effectively. First, the method decomposes training samples of each class into several subclasses based on the outputs of classifiers to represent a class as multiple models, and estimates a localized template by averaging the outputs for each subclass. The distances between a test sample and templates are then calculated. Lastly, the test sample is assigned to the class that is most frequently represented among the k most similar templates. In this paper, C-means clustering algorithm is used as the decomposition method, and k is automatically chosen according to the intra-class compactness and inter-class separation of a given data set. Since the proposed method uses multiple models per class and refers to k models rather than matches with the most similar one, it could obtain stable and high accuracy. In this paper, experiments on UCI and ELENA database showed that the proposed method performed better than conventional fusion methods.