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Sine Cosine Algorithm for Feature Selection

View Sine Cosine Algorithm for Feature Selection on File Exchange License GitHub release

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Introduction

  • This toolbox offers a Sine Cosine Algorithm ( SCA ) method
  • The Main file illustrates the example of how SCA 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 solutions
  • max_Iter : maximum number of iterations
  • alpha : constant

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; 
alpha    = 2;

% Sine Cosine Algorithm
[sFeat,Sf,Nf,curve] = jSCA(feat,label,N,max_Iter,alpha,HO);

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

Requirement

  • MATLAB 2014 or above
  • Statistics and Machine Learning Toolbox

About

Application of Sine Cosine Algorithm (SCA) in the feature selection tasks.

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