Figure 3.

Prediction accuracies of two different feature selection approaches (outside of CV and within CV) for SVM and RLDA (unit: %) with respect to the number of selected features for the actual clinical EEG data. Note that features were sequentially selected with higher votes, and the corresponding features were independently used to estimate classification accuracies for each feature number. As the number of features selected by the within CV feature selection method varied in each CV loop, we averaged the prediction accuracies of a specific number of features selected in each CV loop. However, features fixed after the feature selection performed outside of CV were used to estimate classification accuracies as a function of the number of features.