Fuzzy Data Augmentation for Handling Overlapped and Imbalanced Data
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-92307-5_73
Reference22 articles.
1. Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5(4), 221–232 (2016). https://doi.org/10.1007/s13748-016-0094-0
2. Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl. 6(1), 20–29 (2004)
3. Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
4. Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. 6, 769–772 (1976)
5. Prati, R.C., Batista, G.E., Monard, M.C.: Class imbalances versus class overlapping: an analysis of a learning system behavior. In: Mexican International Conference on Artificial Intelligence. Springer (2004)
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