On the design of an ellipsoid ARTMAP classifier within the fuzzy adaptive system ART framework

In this paper we present the design of fuzzy adaptive system ellipsoid ARTMAP (FASEAM), a novel neural architecture based on ellipsoid ARTMAP (EAM) that is equipped with concepts utilized in the fuzzy adaptive system ART (FASART) architecture. More specifically, we derive a new category choice function appropriate for EAM categories that is non-constant in a category´s representation region. Additionally, we augment the EAM category description with a centroid vector, whose learning rate is inversely proportional to the number of training patterns accessing the category. Finally, we demonstrate the merits of our design choices by comparing FASART, EAM and FASEAM in terms of generalization performance and final structural complexity on a set of classification problems.