A dataset is imbalanced if the classification categories are not
approximately equally represented. This paper shows that a combination of
our method of adaptive over-sampling of the minority (abnormal) class and
under-sampling of the majority (normal) class can achieve a good
classification performance. The method is evaluated using the Receiver
Operating Characteristic (ROC) technique. Our method is an extension of a
recently proposed state-of-the-art approach, SMOTE, and the result shows
that it outperforms SMOTE, on three imbalanced datasets.