Whale-Optimization-Algorithm-for-Feature-Selection

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Whale-Optimization-Algorithm-for-Feature-Selection

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Application of Whale Optimization Algorithm (WOA) in the feature selection tasks.

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# Whale Optimization Algorithm for Feature Selection

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## Introduction
* This toolbox offers a Whale Optimization Algorithm ( WOA ) method
* The `Main` script illustrates the example of how WOA can solve the feature selection problem using benchmark dataset

## Input
* *`feat`*     : feature vector ( Instances *x* Features )
* *`label`*    : label vector ( Instances *x* 1 )
* *`N`*        : number of whales
* *`max_Iter`* : maximum number of iterations

## Output
* *`sFeat`*    : selected features
* *`Sf`*       : selected feature index
* *`Nf`*       : number of selected features
* *`curve`*    : convergence curve

### Example
```code
% 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;

% Whale Optimization Algorithm
[sFeat,Sf,Nf,curve] = jWOA(feat,label,N,max_Iter,HO);

% Accuracy
Acc = jKNN(sFeat,label,HO); 
fprintf('\n Accuracy: %g %%',Acc);

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

## Requirement
* MATLAB 2014 or above
* Statistics and Machine Learning Toolbox

## Cite As
```code
@article{too2021spatial,
  title={Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach},
  author={Too, Jingwei and Mafarja, Majdi and Mirjalili, Seyedali},
  journal={Neural Computing and Applications},
  pages={1--22},
  year={2021},
  publisher={Springer}
}
```

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