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On combining multiple classifiers by fuzzy templates
Conference proceeding

On combining multiple classifiers by fuzzy templates

Ludmila I. Kuncheva, James C. Bezdek and Melanie A. Sutton
1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS : August 20 & 21, 1998, Pensacola Beach, Florida, USA (Cat. No.98TH8353), pp.193-197
Conference of the North American Fuzzy Information Processing Society, NAFIPS (Pensacola Beach, Florida, USA, 08/20/1998–08/21/1998)
1998
DOI: https://doi.org/10.1109/NAFIPS.1998.715563
Web of Science ID: WOS:000077524200039

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Abstract

We study classifier fusion by the fuzzy template (FT) technique. Given an object to be classified, each classifier from the pool yields a vector with degrees of “support” for the classes, thereby forming a decision profile. A fuzzy template is associated with each class as the averaged decision profile over the training samples from this class. A new object is then labeled with the class whose fuzzy template is closest to the objects’ decision profile. We give a brief overview of the field to place the FT approach in a proper group of classifier combination techniques. Experiments with two data sets (satimage and phoneme) from the ELENA database demonstrate the superior performance of FT over: a version of majority voting; aggregation by fuzzy connectives (minimum, maximum, and product); and (unweighted) average.

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