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. Attached Files Name Description MIMEType Size Downloads 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 Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 03 Sep 2012, 15:28:58 EST Fri, 12 Oct 2012, 09:31:30 EST Fri, 12 Oct 2012, 09:33:22 EST Thu, 11 Dec 2014, 18:18:07 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 110 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 03 Sep 2012, 15:28:58 EST

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