In recent years, imbalanced class datasets have caused many difficulties influencing on the analysis and understanding of raw data, which support decision-making process in many domains, especially in biomedical data classifications. Although there were a few approaches achieving promising results in applying class imbalance learning methods, this issue has still not solved completely and successfully yet by the existing methods. SMOTE is a famous and general over-sampling method addressing this problem, however, in some cases it cannot improve or sometimes reduces classification performance. Therefore, we developed a novel method named SPY. Experimental results on five imbalanced benchmark datasets from the UCI Machine Learning Repository showed that our method achieved better sensitivity and G-mean values than the control method (i.e., no over-sampling), SMOTE, and several successors of modified SMOTE including safe-level-SMOTE, safe-SMOTE, and borderline-SMOTE.
['Xuan Tho Dang', 'Dang Hung Tran', 'Osamu Hirose', 'Kenji Satou']
SPY: A Novel Resampling Method for Improving Classification Performance in Imbalanced Data