Binary Grey Wolf Optimization for Feature Selection

Introduction

• This toolbox offers two types of binary grey wolf optimization methods
  • BGWO1
  • BGWO2
• The Main file demos the examples of how BGWO solves the feature selection problem using benchmark data-set

Input

• feat : feature vector ( Instances x Features )
• label : label vector ( Instances x 1 )
• N : number of wolves
• max_Iter : maximum number of iterations

Output

• sFeat : selected features
• Sf : selected feature index
• Nf : number of selected features
• curve : convergence curve

Example

% Benchmark data set 
load ionosphere.mat; 

% Set 20% data as validation set
ho = 0.2; 
% Hold-out method
HO = cvpartition(label,'HoldOut',ho);

% Parameter setting
N        = 10; 
max_Iter = 100;
% Binary Grey Wolf Optimization
[sFeat,Sf,Nf,curve] = jBGWO2(feat,label,N,max_Iter,HO); 

% Plot convergence curve
plot(1:max_Iter,curve); 
xlabel('Number of Iterations');
ylabel('Fitness Value');
title('BGWO2'); grid on;

Requirement

• MATLAB 2014 or above
• Statistics and Machine Learning Toolbox

Please consider citing my article

@article{too2018new,
  title={A new competitive binary Grey Wolf Optimizer to solve the feature selection problem in EMG signals classification},
  author={Too, Jingwei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Mohd Ali, Nursabillilah and Tee, Weihown},
  journal={Computers},
  volume={7},
  number={4},
  pages={58},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}


@article{too2020opposition,
  title={Opposition based competitive grey wolf optimizer for EMG feature selection},
  author={Too, Jingwei and Abdullah, Abdul Rahim},
  journal={Evolutionary Intelligence},
  pages={1--15},
  year={2020},
  publisher={Springer}
}