MicroARTMAP: Use of Mutual Information for Category Reduction in Fuzzy ARTMAP

A new architecture, called MicroARTMAP, is proposed to impact the category proliferation problem present i n F uzzy ARTMAP. It handles probabilistic information through the optimization of the mutual information between the input and output spaces, but allowing a small training error, thus avoiding overrtting. While reducing the number of categories used by F uzzy ARTMAP, it holds several desirable properties, such as a correct treatment of exceptions and a fast algorithm, as opposed to other approaches like BARTMAP. In addition, it is shown that MicroARTMAP is less sensitive than Fuzzy ARTMAP with respect to the the pattern presentation order, and that it degrades less if the training set is noisy.