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

Tan, Shing Chiang, Rao, M. V. C. and Lim, Chee Peng 2008, A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm, Soft computing, vol. 12, no. 8, pp. 765-775.

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Title A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm
Author(s) Tan, Shing Chiang
Rao, M. V. C.
Lim, Chee Peng
Journal name Soft computing
Volume number 12
Issue number 8
Start page 765
End page 775
Total pages 11
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2008-06
ISSN 1432-7643
1433-7479
Keyword(s) adaptive resonance theory
dynamic decay adjustment algorithm
fuzzy ARTMAP
ordering algorithm
Summary 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.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048091

Document type: Journal Article
Collection: Institute for Frontier Materials
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