Title

An Ordering Algorithm For Pattern Presentation In Fuzzy Artmap That Tends To Improve Generalization Performance

Keywords

Fuzzy ARTMAP; Generalization; Learning; Max-min clustering

Abstract

In this paper we introduce a procedure, based on the max-min clustering method, that identifies a fixed order of training pattern presentation for fuzzy adaptive resonance theory mapping (ARTMAP). This procedure is referred to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered fuzzy ARTMAP exhibits a generalization performance that is better than the average generalization performance of fuzzy ARTMAP, and in certain cases as good as, or better than the best fuzzy ARTMAP generalization performance. We also calculate the number of operations required by the ordering algorithm and compare it to the number of operations required by the training phase of fuzzy ARTMAP. We show that, under mild assumptions, the number of operations required by the ordering algorithm is a fraction of the number of operations required by fuzzy ARTMAP. © 1999 IEEE.

Publication Date

12-1-1999

Publication Title

IEEE Transactions on Neural Networks

Volume

10

Issue

4

Number of Pages

768-778

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/72.774217

Socpus ID

0032657707 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0032657707

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