Binary Harris Hawk Optimization for Feature Selection

Introduction

• This toolbox offers Binary Harris Hawk Optimization ( BHHO )
• The Main file illustrates the example of how BHHO can solve the feature selection problem using benchmark data-set.

Input

• feat : feature vector ( Instances x Features )
• label : label vector ( Instances x 1 )
• N : number of hawks
• 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 Harris Hawk Optimization
[sFeat,Sf,Nf,curve] = jBHHO(feat,label,N,max_Iter,HO);

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

Requirement

• MATLAB 2014 or above
• Statistics and Machine Learning Toolbox

Cite As

@article{too2019new,
  title={A new quadratic binary harris hawk optimization for feature selection},
  author={Too, Jingwei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah},
  journal={Electronics},
  volume={8},
  number={10},
  pages={1130},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}