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A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

journal contribution
posted on 2008-06-01, 00:00 authored by S Tan, M Rao, Chee Peng LimChee Peng Lim
This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.

History

Journal

Soft computing

Volume

12

Issue

8

Pagination

765 - 775

Publisher

Springer

Location

Heidelberg, Germany

ISSN

1432-7643

eISSN

1433-7479

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2007, Springer-Verlag