On combining multiple classifiers by fuzzy templates
- 27 November 2002
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The authors study classifier fusion using 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. They 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.Keywords
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