1. N. Rout, D. Mishra, M.K. Mallick, Handling Imbalanced Data: A Survey, in International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications pp. 431–443, (2018)
2. J. Zhang, I. Mani, KNN approach to unbalance data distributions: A case study involving information extraction, in Proceedings 12th Int. Conf. Machine Learning - Workshop on Learning from Imbalanced Datasets II (Washington DC, USA, 2003), pp. 42–48
3. N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: synthetic minority over-sampling 7 technique. J. Artif. Intell. Res. 16(1), 321–357 (2002)
4. H. Han, W. Wang, B. Mao, Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning, in Proceedings of the International Conference on Intelligent Computing (ICIC’05), vol. 3644 of Lecture Notes in Computer Science, pp. 878–887, (2005)
5. H. He, Y. Bai, E.A. Garcia, S. Li. ADASYN: Adaptive synthetic sampling approach for imbalanced learning, in IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328, (2008)