Safe-μARTMAP: a new solution for reducing category proliferation in fuzzy ARTMAP

μARTMAP is a neural network architecture that addresses the category proliferation problem present in fuzzy ARTMAP, by encouraging the creation of large hyperboxes. However, under certain characteristics of the classification task, this principle can be inadequate, namely if some classes have their patterns distributed in several isolated regions, far apart in the input space. Here we propose Safe-μARTMAP, a generalization of μARTMAP that limits the growth of a category in response to a single pattern, so that large hyperboxes are not created under these conditions. Experimental results confirm that the performance improves in some synthetic and real world tasks