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Computers 2018, 7(4), 58; https://doi.org/10.3390/computers7040058

A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

1
Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya 76100, Durian Tunggal, Melaka, Malaysia
2
Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya 76100, Durian Tunggal, Melaka, Malaysia
*
Authors to whom correspondence should be addressed.
Received: 15 October 2018 / Revised: 1 November 2018 / Accepted: 2 November 2018 / Published: 5 November 2018
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Abstract

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. View Full-Text
Keywords: feature selection; electromyography; grey wolf optimizer; binary grey wolf optimization; classification; time-frequency feature feature selection; electromyography; grey wolf optimizer; binary grey wolf optimization; classification; time-frequency feature
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Too, J.; Abdullah, A.R.; Mohd Saad, N.; Mohd Ali, N.; Tee, W. A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification. Computers 2018, 7, 58.

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