ARCHIVES

1. I. Dal Pozzolo, O. Bontempi, and G. Snoeck, “Adversarial drift detection,” IEEE International Conference on Data Mining Works hops
(ICDMW), pp. 417–426, 2015.
2. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, pp. 886–893, 2005.
3. I. Goodfellow et al., “Generative adversarial nets,” Advances in Neural Information Processing Systems (NIPS), vol. 27, pp. 2672–2680,
2014.
4. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
5. A. Dal Pozzolo, C. Bontempi, and G. Snoeck, “Calibrating probability with undersampling for unbalanced classification,” IEEE Symposium
Series on Computational Intelligence, pp. 159–166, 2015.
6. J. West and M. Bhattacharya, “Intelligent financial fraud detection: A comprehensive review,” Computers & Security, vol. 57, pp. 47–66,
2016.
7. S. Kaggle, “Credit Card Fraud Detection Dataset,” Kaggle, 2020. [Online]. Available: https://www.kaggle.com
8. S. Bahnsen, D. Aouada, and B. Ottersten, “Costsensitive decision trees for fraud detection,” Expert Systems with Applications, vol. 39, no.
16, pp. 11817–11825, 2012.
9. A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and Systems for Video
Technology, vol. 14, no. 1, pp. 4–20, Jan. 2004.
10. R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” IEEE Symposium on
Security and Privacy, pp. 305–316, 2010.
11. A. Rajput, S. Muralidhar, and R. Shastri, “UPI—A digital innovation in Indian banking system,” International Journal of Engineering
Research & Technology, vol. 7, no. 4, pp. 1–5, 2018.
12. M. Carcillo, Y. Boulanger, and G. Bontempi, “Scarff: A scalable framework for streaming credit card fraud detection,” IEEE Transactions
on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1446–1459, Apr. 2021.