Title

An Experimental Comparison Of Semi-Supervised Artmap Architectures, Gcs And Gng Classifiers

Abstract

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. © 2005 IEEE.

Publication Date

12-1-2005

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

5

Number of Pages

3121-3126

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2005.1556426

Socpus ID

33750136378 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33750136378

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