References

1. Bae, W.D., Alfonso, A., Stanko, D., Hao, L., Le, L., Horak, M.: Improving classification performance on rare events in data starved medical applications. In: 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). pp. 1–6. IEEE (2023)

2. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)

3. Gretel: Gretel (Accessed: January 2024), https://gretel.ai/

4. Huang, Y., Fields, K.G., Ma, Y.: A tutorial on generative adversarial networks with application to classification of imbalanced data. Statistical Analysis and Data Mining: The ASA Data Science Journal (2021)

5. Kamalov, F., Denisov, D.: Gamma distribution-based sampling for imbalanced data. Knowledge-Based Systems 207, 106368 (2020)

6. Lee, H., Kim, J., Kim, S.: Gaussian-based smote algorithm for solving skewed class distributions. International Journal of Fuzzy Logic and Intelligent Systems 17(4), 229–234 (2017)

7. Wan, Q., Deng, X., Li, M., Yang, H.: Sddsmote: Synthetic minority oversampling technique based on sample density distribution for enhanced classification on imbalanced microarray data. In: The 6th International Conf. on Compute and Data Analysis. pp. 35–42 (2022)

8. Woo, J., Rudasingwa, G., Kim, S.: Assessment of daily personal pm2. 5 exposure level according to four major activities among children. Applied Sciences 10(1), 159 (2020)

9. Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional gan. Advances in neural information processing systems 32 (2019)