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A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this articl...

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Main Authors: Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah, Mohd Ali, Nursabillilah, Tee, Wei Hown
Format: Article
Language:English
Published: MDPI AG 2018
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utem.edu.my/id/eprint/23003/2/A%20New%20Competitive%20Binary%20GreyWolf%20Optimizer.pdf
http://eprints.utem.edu.my/id/eprint/23003/
https://www.mdpi.com/2073-431X/7/4/58/htm
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spelling my.utem.eprints.230032021-08-09T18:25:36Z http://eprints.utem.edu.my/id/eprint/23003/ A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification Too, Jing Wei Abdullah, Abdul Rahim Mohd Saad, Norhashimah Mohd Ali, Nursabillilah Tee, Wei Hown T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application. MDPI AG 2018-11 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/23003/2/A%20New%20Competitive%20Binary%20GreyWolf%20Optimizer.pdf Too, Jing Wei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Mohd Ali, Nursabillilah and Tee, Wei Hown (2018) A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification. Computers, 7 (4). pp. 1-18. ISSN 2073-431X https://www.mdpi.com/2073-431X/7/4/58/htm 10.3390/computers7040058
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Too, Jing Wei
Abdullah, Abdul Rahim
Mohd Saad, Norhashimah
Mohd Ali, Nursabillilah
Tee, Wei Hown
A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
description Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.
format Article
author Too, Jing Wei
Abdullah, Abdul Rahim
Mohd Saad, Norhashimah
Mohd Ali, Nursabillilah
Tee, Wei Hown
author_facet Too, Jing Wei
Abdullah, Abdul Rahim
Mohd Saad, Norhashimah
Mohd Ali, Nursabillilah
Tee, Wei Hown
author_sort Too, Jing Wei
title A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
title_short A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
title_full A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
title_fullStr A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
title_full_unstemmed A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
title_sort new competitive binary grey wolf optimizer to solve the feature selection problem in emg signals classification
publisher MDPI AG
publishDate 2018
url http://eprints.utem.edu.my/id/eprint/23003/2/A%20New%20Competitive%20Binary%20GreyWolf%20Optimizer.pdf
http://eprints.utem.edu.my/id/eprint/23003/
https://www.mdpi.com/2073-431X/7/4/58/htm
_version_ 1709671923377504256
score 13.211869

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