Boosted ARTMAP

We present a modification to the fuzzy ARTMAP neural network architecture for conducting boosted learning in a probabilistic setting. We call this new architecture boosted ARTMAP (BARTMAP). Performance comparison with fuzzy ARTMAP, PROBART and ART-EMAP on some simple two-class problems is discussed. Experimental results indicate that BARTMAP gives better generalization results on some problems involving classification overlap. In addition BARTMAP requires fewer resources, i.e., network nodes, to achieve performance levels comparable to those in fuzzy ARTMAP.