Figure 2.

Overview of the proposed classification model. In this model, a training set and a test set were derived from the dataset using data points from both majority and minority classes (shown in the left rectangle of the figure). A combination of oversampling and undersampling technique was applied to the training set to generate a resampled training set. The training set in each cross-validation iteration was resampled three times to reduce the bias due to random dataset generation. Then feature selection was applied to select the most discriminative features. Then the classification model was trained on the dimension-reduced training set, and evaluated on the test set.