References
- V. García, R. A. Mollineda, and J. S. Sánchez, Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions, in Pattern Recognition and Image Analysis, vol. 5524, H. Araujo, A. M. Mendonça, A. J. Pinho, and M. I. Torres, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 441–448. Accessed: Mar. 18, 2023. [Online]. Available at http://link.springer.com/10.1007/978-3-642-02172-5_57.
- T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/j.patrec.2005.10.010.
- Y.-A. Le Borgne, W. Siblini, B. Lebichot, and G. Bontempi, Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook. Université Libre de Bruxelles, 2022. [Online]. Available at https://github.com/Fraud-Detection-Handbook/fraud-detection-handbook.
- W. Siblini, J. Fréry, L. He-Guelton, F. Oblé, and Y.-Q. Wang, Master your Metrics with Calibration, vol. 12080, 2020, pp. 457–469. doi: 10.1007/978-3-030-44584-3_36.
- Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou, Exploratory Undersampling for Class-Imbalance Learning, IEEE Trans. Syst., Man, Cybern. B, vol. 39, no. 2, pp. 539–550, Apr. 2009, doi: 10.1109/TSMCB.2008.2007853.
- M. S. Santos, J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos, Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier], IEEE Comput. Intell. Mag., vol. 13, no. 4, pp. 59–76, Nov. 2018, doi: 10.1109/MCI.2018.2866730.
- A. Fernández, S. García, M. Galar, R. Prati, B. Krawczyk, and F. Herrera, Learning from Imbalanced Data Sets. Springer International Publishing, 2018