Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers

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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Main Authors: Hassan, Raini, Taha Alshaikhli, Imad Fakhri, Ahmad, Salmiah
Format: Article
Language:English
Published: University of El Oued 2017
Subjects:
Q Science (General)
QA Mathematics
Online Access: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
id iium-61236
recordtype eprints
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
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic Q Science (General)
QA Mathematics
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
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.
format Article
author Hassan, Raini
Taha Alshaikhli, Imad Fakhri
Ahmad, Salmiah
author_facet Hassan, Raini
Taha Alshaikhli, Imad Fakhri
Ahmad, Salmiah
author_sort Hassan, Raini
title 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_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_sort input significance analysis: feature ranking through synaptic weights manipulation for anns-based classifiers
publisher University of El Oued
publishDate 2017
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
first_indexed 2023-09-18T21:26:51Z
last_indexed 2023-09-18T21:26:51Z
_version_ 1777412248770183168

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