Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers
Due to the ANNs architecture, the ISA methods that can manipulate synaptic weights selected are Connection Weights (CW) and Garson’s Algorithm (GA). The ANNs-based classifiers that can provide such manipulation are Multi Layer Perceptron (MLP) and Evolving Fuzzy Neural Networks (EFuNNs). The goals f...
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| Format: | Article |
| Language: | English |
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University of El Oued
2017
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| Online Access: | http://irep.iium.edu.my/61236/ http://irep.iium.edu.my/61236/1/2945-7342-1-PB.pdf |
| _version_ | 1848785635951771648 |
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| author | Hassan, Raini Taha Alshaikhli, Imad Fakhri Ahmad, Salmiah |
| author_facet | Hassan, Raini Taha Alshaikhli, Imad Fakhri Ahmad, Salmiah |
| author_sort | Hassan, Raini |
| building | IIUM Repository |
| collection | Online Access |
| description | Due to the ANNs architecture, the ISA methods that can manipulate synaptic weights selected are Connection Weights (CW) and Garson’s Algorithm (GA). The ANNs-based classifiers that can provide such manipulation are Multi Layer Perceptron (MLP) and Evolving Fuzzy Neural Networks (EFuNNs). The goals for this work are firstly to identify which of the two classifiers works best with the filtered/ranked data, secondly is to test the FR method by using a selected dataset taken from the UCI Machine Learning Repository and in an online environment and lastly to attest the FR results by using another selected dataset taken from the same source and in the same environment. There are three groups of experiments conducted to accomplish these goals. The results are promising when FR is applied, some efficiency and accuracy are noticeable compared to the original data. |
| first_indexed | 2025-11-14T16:56:17Z |
| format | Article |
| id | iium-61236 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:56:17Z |
| publishDate | 2017 |
| publisher | University of El Oued |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-612362019-12-03T10:26:11Z http://irep.iium.edu.my/61236/ Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers Hassan, Raini Taha Alshaikhli, Imad Fakhri Ahmad, Salmiah Q Science (General) QA Mathematics Due to the ANNs architecture, the ISA methods that can manipulate synaptic weights selected are Connection Weights (CW) and Garson’s Algorithm (GA). The ANNs-based classifiers that can provide such manipulation are Multi Layer Perceptron (MLP) and Evolving Fuzzy Neural Networks (EFuNNs). The goals for this work are firstly to identify which of the two classifiers works best with the filtered/ranked data, secondly is to test the FR method by using a selected dataset taken from the UCI Machine Learning Repository and in an online environment and lastly to attest the FR results by using another selected dataset taken from the same source and in the same environment. There are three groups of experiments conducted to accomplish these goals. The results are promising when FR is applied, some efficiency and accuracy are noticeable compared to the original data. University of El Oued 2017-10-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/61236/1/2945-7342-1-PB.pdf Hassan, Raini and Taha Alshaikhli, Imad Fakhri and Ahmad, Salmiah (2017) Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers. Journal of Fundamental and Applied Sciences, 9 (4S). pp. 639-662. E-ISSN 1112-9867 http://www.jfas.info/index.php/jfas/article/view/2945 10.4314/jfas.v9i4s.37 |
| spellingShingle | Q Science (General) QA Mathematics Hassan, Raini Taha Alshaikhli, Imad Fakhri Ahmad, Salmiah Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers |
| title | Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers |
| title_full | Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers |
| title_fullStr | Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers |
| title_full_unstemmed | Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers |
| title_short | Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers |
| title_sort | input significance analysis: feature ranking through synaptic weights manipulation for anns-based classifiers |
| topic | Q Science (General) QA Mathematics |
| url | http://irep.iium.edu.my/61236/ http://irep.iium.edu.my/61236/ http://irep.iium.edu.my/61236/ http://irep.iium.edu.my/61236/1/2945-7342-1-PB.pdf |