On combining multiple classifiers by fuzzy templates

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 averagd 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.