Feature selection SVM-Recursive Feature elimination (SVM-RFE) with Libsvm, the accuracy result is worse than without feature selection, why?

Question

I'm trying to use SVM-RFE with libsvm library to run on the gene expression dataset. My algorithm is written in Matlab. The particular dataset able to produce 80++% of classification accuracy under the 5-fold CV without applied Feature selection. When I tried to apply svm-rfe on this dataset (same svm parameter setting and use 5-fold CV), the classification result become worse, can only achieve 60++% classification accuracy.

Here is my matlab coding, appreciate if anyone can shed some light on what's wrong with my codes. Thank you in advance.

[label, data] = libsvmread('libsvm_data.scale');
[N D] = size(data);

numfold=5; 
indices = crossvalind ('Kfold',label, numfold);
cp = classperf(label);

for i= 1:numfold

disp(strcat('Fold-',int2str(i)));
testix = (indices == i); trainix = ~testix;
test_data = data(testix,:);  test_label = label(testix);
train_data = data(trainix,:); train_label = label(trainix);

model = svmtrain(train_label, train_data, sprintf('-s 0 -t 0);    %'

s = 1:D;
r = [];
iter = 1;

    while ~isempty(s)

    X = train_data(:,s);

    fs_model = svmtrain(train_label, X, sprintf('-s 0 -t %f -c %f -g %f -b 1', kernel, cost, gamma));

    w = fs_model.SVs' * fs_model.sv_coef;    %'
    c = w.^2;
    [c_minvalue, f] = min(c);
    r = [s(f),r];
   ind = [1:f-1, f+1:length(s)];
    s = s(ind);

    iter = iter + 1;
    end

    predefined = 100;
   important_feat = r(:,D-predefined+1:end);

    for l=1:length(important_feat)
        testdata(:,l) = test_data (:,important_feat(l));
    end


 [predict_label_itest, accuracy_itest, prob_values] = svmpredict(test_label, testdata, model,'-b 1'); 
acc_itest_fs (:,i) = accuracy_itest(1);

  clear testdata;
end

Mean_itest_fs = mean((acc_itest_fs),2);
Mean_bac_fs = mean (bac_fs,2);  

Answer 1

After applying RFE to the traindata, you get a subset of the traindata. So when you use the traindata to train a model, I think you should use the subset of the traindata to train this model.