- This toolbox offers a Binary Atom Search Optimization ( BASO )
- The
Mainfile illustrates the example of how BASO can solve the feature selection problem using benchmark data-set.
feat: feature vector ( Instances x Features )label: label vector ( Instances x 1 )N: number of atomsmax_Iter: maximum number of iterationsalpha: depth weightbeta: multiplier weight
sFeat: selected featuresSf: selected feature indexNf: number of selected featurescurve: convergence curve
% 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 = 50; beta = 0.2; % Binary Atom Search Optimization [sFeat,Sf,Nf,curve] = jBASO(feat,label,N,max_Iter,alpha,beta,HO); % Plot convergence curve plot(1:max_Iter,curve); xlabel('Number of iterations'); ylabel('Fitness Value'); title('BASO'); grid on; - MATLAB 2014 or above
- Statistics and Machine Learning Toolbox
@article{too2020binary, title={Binary atom search optimisation approaches for feature selection}, author={Too, Jingwei and Rahim Abdullah, Abdul}, journal={Connection Science}, pages={1--25}, year={2020}, publisher={Taylor \& Francis} }