An experimental comparison of semi-supervised ARTMAP architectures, GCS and GNG classifiers

In this paper we present an experimental comparison of four neural-based classifiers, namely growing cell structures (GCS), growing neural gas (GNG), semi-supervised fuzzy ARTMAP (ssFAM) and semi-supervised ellipsoid ARTMAP (ssEAM). The comparison is performed in terms of classification accuracy and structural complexity of the resulting classifiers. Earlier studies that had appeared in the literature showed that fuzzy ARTMAP, which utilizes fully-supervised learning, may suffer from poor generalization performance, when compared to GCS and GNG classifiers. This phenomenon typically occurs, when class distribution overlap is significant. Here, we present new results indicating that ARTMAP classifiers equipped with semi-supervised learning capabilities can improve their performance with respect to GCS and GNG classifiers, while maintaining lower structural complexity.