A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm
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 lim...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SPRINGER
2008
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2666/ http://shdl.mmu.edu.my/2666/1/768.pdf |
| _version_ | 1848790118450593792 |
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| author | Tan, Shing Chiang Rao, M. V. C. Lim, Chee Peng |
| author_facet | Tan, Shing Chiang Rao, M. V. C. Lim, Chee Peng |
| author_sort | Tan, Shing Chiang |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:07:32Z |
| format | Article |
| id | mmu-2666 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:07:32Z |
| publishDate | 2008 |
| publisher | SPRINGER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-26662014-02-25T03:07:17Z http://shdl.mmu.edu.my/2666/ A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm Tan, Shing Chiang Rao, M. V. C. Lim, Chee Peng T Technology (General) QA75.5-76.95 Electronic computers. Computer science 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. SPRINGER 2008-06 Article NonPeerReviewed text en http://shdl.mmu.edu.my/2666/1/768.pdf Tan, Shing Chiang and 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, 12 (8). pp. 765-775. ISSN 1432-7643 http://dx.doi.org/10.1007/s00500-007-0235-2 doi:10.1007/s00500-007-0235-2 doi:10.1007/s00500-007-0235-2 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Tan, Shing Chiang Rao, M. V. C. Lim, Chee Peng A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm |
| title | A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm |
| title_full | A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm |
| title_fullStr | A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm |
| title_full_unstemmed | A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm |
| title_short | A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm |
| title_sort | hybrid neural network classifier combining ordered fuzzy artmap and the dynamic decay adjustment algorithm |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2666/ http://shdl.mmu.edu.my/2666/ http://shdl.mmu.edu.my/2666/ http://shdl.mmu.edu.my/2666/1/768.pdf |