scholarly journals Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4065 ◽  
Author(s):  
Chao Zhang ◽  
Wen Wang ◽  
Yong Pan
Keyword(s):  
Feature Selection ◽  
Electronic Nose ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Feature Subset ◽  
Grey Wolf ◽  
Electronic Noses ◽  
Selection Algorithms ◽  
Multiple Assessment ◽  
Performance Of Algorithm

Electronic nose is a kind of widely-used artificial olfactory system for the detection and classification of volatile organic compounds. The high dimensionality of data collected by electronic noses can hinder the process of pattern recognition. Thus, the feature selection is an essential stage in building a robust and accurate model for gas recognition. This paper proposed an improved grey wolf optimizer (GWO) based algorithm for feature selection and applied it on electronic nose data for the first time. Two mechanisms are employed for the proposed algorithm. The first mechanism contains two novel binary transform approaches, which are used for searching feature subset from electronic nose data that maximizing the classification accuracy while minimizing the number of features. The second mechanism is based on the adaptive restart approach, which attempts to further enhance the search capability and stability of the algorithm. The proposed algorithm is compared with five efficient feature selection algorithms on three electronic nose data sets. Three classifiers and multiple assessment indicators are used to evaluate the performance of algorithm. The experimental results show that the proposed algorithm can effectively select the feature subsets that are conducive to gas recognition, which can improve the performance of the electronic nose.

Feature Selection Method Based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization

2012 ◽  
Vol 546-547 ◽  
pp. 1538-1543 ◽  
Author(s):  
Chao Chen ◽  
Hao Dong Zhu
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Data Sets ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Memory Space ◽  
Selection Algorithms

In order to enhance the operating speed and reduce the occupied memory space and filter out irrelevant or lower degree of features, feature selection algorithms must be used. However, most of existing feature selection methods are serial and are inefficient timely to be applied to massive text data sets, so it is a hotspot how to improve efficiency of feature selection by means of parallel thinking. This paper presented a feature selection method based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization (PBIQPSO). The presented method uses the Binary Immune Quantum-Behaved Particle Swarm Optimization to select feature subset, takes advantage of multiple computing nodes to enhance time efficiency, so can acquire quickly the feature subsets which are more representative. Experimental results show that the method is effective.

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

Computers ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 58 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Abdullah ◽  
Norhashimah Mohd Saad ◽  
Nursabillilah Mohd Ali ◽  
Weihown Tee
Keyword(s):  
Feature Selection ◽  
Feature Reduction ◽  
Selection Problem ◽  
Grey Wolf Optimizer ◽  
Binary Particle Swarm Optimization ◽  
Feature Subset ◽  
Grey Wolf ◽  
Feature Selection Problem ◽  
Time Frequency ◽  
Emg Signal

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.

scholarly journals A novel feature selection algorithm based on damping oscillation theory

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255307
Author(s):  
Fujun Wang ◽  
Xing Wang
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Euclidean Distance ◽  
Oscillation Theory ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Data Sets ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Filter Model

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.

A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

2020 ◽  
Vol 139 ◽  
pp. 112824 ◽  
Author(s):  
Mohamed Abdel-Basset ◽  
Doaa El-Shahat ◽  
Ibrahim El-henawy ◽  
Victor Hugo C. de Albuquerque ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Two Phase ◽  
Phase Mutation

scholarly journals Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1845 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Rujiang Hao ◽  
Zechao Liu
Keyword(s):  
Frequency Band ◽  
Background Noise ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Rolling Element Bearings ◽  
Grey Wolf ◽  
Objective Functions ◽  
Multi Objective ◽  
Rolling Element ◽  
Envelope Spectrum

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

scholarly journals MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 107635-107649 ◽  
Author(s):  
El-Sayed M. El-Kenawy ◽  
Marwa Metwally Eid ◽  
Mohamed Saber ◽  
Abdelhameed Ibrahim
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Stochastic Fractal Search
Sign in / Sign up

Export Citation Format

Share Document