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A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification
2018
Computers
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
doi:10.3390/computers7040058
fatcat:7iwazouwpnezrahs4lx4hmzqce