Read the full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49809 DOI: 10.4236/jbise.2014.711090 Author(s) Lan Anh T. Nguyen, Xuan Tho Dang, Tu Kien T. Le, Thammakorn Saethang, Vu Anh Tran, Duc Luu Ngo,Sergey Gavrilov, Ngoc Giang Nguyen, Mamoru Kubo, Yoichi Yamada, Kenji Satou ABSTRACT β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due to the imbalanced dataset, the performance is still inadequate. In this study, we proposed a novel over-sampling technique FOST to deal with the class-imbalance problem. Experimental results on three standard benchmark datasets showed that our method is comparable with state-of-the-art methods. EWW140923GJR In addition, we applied our algorithm to five benchmark datasets from UCI Machine Learning Repository and achieved significant improvement in G-mean and Sensitivity. It means that our method is also effective for various imbalanced data other than β-turns and β-turn types. KEYWORDS Beta-Turns, Beta-Turn Types, Class-Imbalance, Over-Sampling