SDGnE: A Synthetic Data Generation and Evaluation System for Rare Event Prediction
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5575-2_49
Reference9 articles.
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. J. Artif. Intell. Res. 16, 321–357 (2002)
3. Gretel: Gretel. Accessed Jan 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. The ASA Data Science Journal, Statistical Analysis and Data Mining (2021)
5. Kamalov, F., Denisov, D.: Gamma distribution-based sampling for imbalanced data. Knowl.-Based Syst. 207, 106368 (2020)
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