One of the most critical and frequent problems in biomedical data classification is imbalanced class distribution, where samples from the majority class significantly outnumber the minority class. SMOTE is a well-known general over-sampling method used to address this problem; however, in some cases it cannot improve or even reduces classification performance. To address these issues, we have developed a novel minority over-sampling method named safe-SMOTE. Experimental results from two gene

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... ression datasets for cancer classification (i.e., colon-cancer and leukemia) and six imbalanced benchmark datasets from the UCI Machine Learning Repository showed that our method achieved better sensitivity and G-mean values than both the control method (i.e., no over-sampling) and SMOTE. For example, in the colon-cancer dataset, although the sensitivity and specificity achieved by SMOTE (81.36% and 88.63%) were lower than for the control method (81.59% and 89.50%), safe-SMOTE in contrast had these values increase (81.82% and 90.50%). Similarly, the G-mean value of the control (85.45%) decreased to 84.91% when SMOTE was employed, but increased to 86.04% when using safe-SMOTE. In the leukemia dataset, SMOTE was able to improve the sensitivity and G-mean values with respect to the control; however, safe-SMOTE achieved noticeable, even greater improvements for both of these criteria.