Geometric SMOTE
Douzas, G., & Bacao, F. (2019). Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE. Information Sciences, 501, 118-135.
Douzas, G., & Bacao, F. (2019). Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE. Information Sciences, 501, 118-135.
Torgo, L., Ribeiro, R.P., Pfahringer, B., Branco, P. (2013). SMOTE for Regression. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg.
Torgo, L., & Ribeiro, R. (2007, September). Utility-based regression. In European conference on principles of data mining and knowledge discovery (pp. 597-604). Springer, Berlin, Heidelberg.
Audibert, J., Michiardi, P., Guyard, F., Marti, S., & Zuluaga, M. A. (2020, August). Usad: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3395-3404).
Yang, Y., Zha, K., Chen, Y. C., Wang, H., & Katabi, D. (2021). Delving into Deep Imbalanced Regression. arXiv preprint arXiv:2102.09554.
Ali-Gombe, A., & Elyan, E. (2019). MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing, 361, 212-221.
OβBrien, R., & Ishwaran, H. (2019). A random forests quantile classifier for class imbalanced data. Pattern recognition, 90, 232-249.
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