Figure 1.

Illustration of the feature selection procedure. This procedure integrate filter and wrapper methods within the subsampling procedure. The optimal features consisted of the features which were most frequently selected in all the subsamples of data. The final optimal feature set was determined by validating classification performance on the training data. We used feature ranking with ANOVA F-value as the filtering process, and the recursive feature elimination algorithm as the wrapping process. A single experiment within a cross-validation (CV) iteration is depicted. SVM = support vector machine.