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

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.

Güncel İkili Optimizasyon Algoritmalarının Kısıtsız Kıyaslama Fonksiyonlarındaki Performans Karşılaştırmaları

“…Özellik seçimi gibi ikili optimizasyon sorunları için GWO'nun ikili sürümü gerekir. Bu yaklaşıma göre, kurt pozisyonunu Denklem 6'daki gibi güncellemek için geçiş operatörünü kullanır [17,18]:…”

“…Burada Crossover (Y1, Y2 ve Y3), çözeltiler arasındaki geçiş işlemidir ve Y1, Y2 ve Y3, sırasıyla alfa, beta ve delta kurtlarının hareketinden etkilenen ikili vektörlerdir. BGWO'da, Y1, Y2 ve Y3 sırasıyla Denklem 7, 10 ve 13 kullanılarak tanımlanır [17,18].…”

“…Algoritma, sonlandırılan kriter karşılanana kadar tekrarlanır. Sonunda, alfa çözümü en uygun özellik olarak seçilir [17,18].…”

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

“…Number of wrongly classified instances Total number of instances Error  (12) where |S| is the length of feature subset, |T| is the total number of features in each dataset, Error is the classification error rate and α is the parameter in [0,1] to control the influence of classification performance and feature size. In this paper, we set α to 0.01 according to [2,3].…”

“…In the process of fitness evaluation, the dataset was randomly partitioned into 80% for the training set and 20% for the testing set [4]. Furthermore, in order to measure the effectiveness of the proposed CBPSO-MIWS, four recent and popular feature selection methods include BPSO [13,22], genetic algorithm (GA) [28], binary gravitational search algorithm (BGSA) [29] and competitive binary grey wolf optimizer (CBGWO) [12] were used in comparison. GA is an evolutionary algorithm that utilizes the selection, crossover and mutation operators to evolve solutions.…”