Supervised learning methods require labeled training data, and in classification problems each data sample belongs to a known class, or category [1, 2]. In a binary classification problem with data samples from two groups, class imbalance occurs when one class, the minority group, contains significantly fewer samples than the other class, the majority group. In many problems [3-7], the minority group is the class of interest, i.e., the positive class. A well-known class imbalanced machine learning scenario is the medical diagnosis task of detecting disease, where the majority of the patients are