Theoretically, having more features implies more discriminative power in classification. However, this
is not always true in practical experience, because not all features are important for understanding or
representing the underlying phenomenon of interest [3]. Please refer to Fig. 1 for the comparison of
classification performance to the number of selected features. It is obvious that selection of a number of
parameters (i.e. features) that represent the optimal position would increase the performance of correct
classification.