Fig. 4

Choice of the optimal constituent classifiers among 500 classifiers. In the pair (x, y), x refers to the number of top-ranked components in the optimal feature set, and y refers to the achieved accuracy using those x components. The optimal numbers of features for the OET-KNN V500 and KNN V500 ensembles were 20 and 21, respectively. The performance started to deteriorate as more votes were accounted for. Overall, the results suggest that the selective voting approach outperforms the all voting approach