Feature Selection

1. Matlab using TreeBagger (it is actually like Random Forest)
==========================================

load ionosphere;

noBag = 5;
myBag  = TreeBagger( noBag  ,   X, Y, 'OOBPred','on' , 'oobvarimp' ,'on' );


//  increase in prediction error if the values of that variable are permuted across OOB observations.
//  The more increase in prediction Error ==> The more important the variable is
oobVarImp = myBag.OOBPermutedVarDeltaError


// re-substitution error
varImp = zeros( noBag, noFeature)
for i=1:noBag
   varimportance( myBag.Trees{i})
end

========== COMPLETE CODE==========

function fromRF

load ionosphere;
noBag = 5;
myBag  = TreeBagger( noBag  ,   X, Y, 'OOBPred','on');
varRanking = zeros( noBag  , size(X,2) ) ;

for i=1:noBag   
   [ val ,varRanking( i , :) ]= sort( varimportance( myBag.Trees{i}) ,'descend')   
end

// suppose finally taking top ranked 5 from all folds
topRank=5;
selectedFeat=[]
for i=1:noBag
   selectedFeat = union( selectedFeat , varRanking( i , 1:topRank) ); 
end

display('done');

end