An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

Issam Dagher, Michael Georgiopoulos, Gregory L. Heileman, George Bebis

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)768-778
Number of pages11
JournalIEEE Transactions on Neural Networks
Volume10
Issue number4
DOIs
StatePublished - 1999
Externally publishedYes

Keywords

  • Fuzzy ARTMAP
  • Generalization
  • Learning
  • Max-min clustering

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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