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FUZZY CLUSTERING WITH AMBIGUITY FOR MULTI-CLASSIFIERS FUSION : CLUSTERING-CLASSIFICATION COOPERATION

Abstract : The main aim of this paper is to demonstrate the performance of multi-classifiers fusion based on fuzzy clustering with ambiguity. The problem is seen from the multi-decision point of view (i.e. several classification modules). Each classification module is specialized on a particular region of the features space. These regions are obtained by fuzzy clustering and constitute the original data set by union.
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https://hal.archives-ouvertes.fr/hal-02092818
Contributor : Veyis Gunes <>
Submitted on : Monday, April 8, 2019 - 2:43:15 PM
Last modification on : Thursday, October 8, 2020 - 11:50:07 AM

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Veyis Gunes, Michel Menard, Pierre Loonis. FUZZY CLUSTERING WITH AMBIGUITY FOR MULTI-CLASSIFIERS FUSION : CLUSTERING-CLASSIFICATION COOPERATION. EUSFLAT-ESTYLF Joint Conference, Sep 1999, Palma de Mallorca, Spain. ⟨hal-02092818⟩

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