1. Lin, W.C., Ts, C.F., Hu, Y.H., et al.: Clustering-based under sampling class-imbalanced data. Inf. Sci. 409, 17–26 (2017)
2. He, H., Bai, Y., Garcia, E.A., et al.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks, pp. 1322–1328 (2008)
3. Galar, M., Fernandez, A., Barrenechea, E., et al.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 463–484 (2011)
4. Zhang, X.C., Jiang, D.X., Han, T., et al.: Rotating machinery fault diagnosis for imbalanced data based on fast clustering algorithm and support vector machine. J. Sens. 2017, 8092691 (2017)
5. Zhang, Y.Y., Li, X.Y., Gao, L., et al.: Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning. J. Manuf. Syst, 48, 34–50 (2018)