Random forest (RF) is an ensemble learning method by generating the multiple decision trees via the bootstrap sampling of training set and random selection of feature subset from the total descriptors (Breiman, 2001). Finally, RF predicts the class based on the consensus votes from these multiple decision trees. In addition, RF can provide the importance of each feature, which is very useful for the intuitive interpretation of the prediction model and is the key criterion for our feature selection in the following section. The number of decision trees (10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1,000) will be probed during the cross-validation.