Author:
Lin Yuan-Fa,Wang Chou-Wen,Wu Chin-Wen
Publisher
Springer Nature Switzerland
Reference11 articles.
1. Baabdullah, T., Alzahrani, A., Rawat, D.B.: On the comparative study of prediction accuracy for credit card fraud detection with imbalanced classifications. In: 2020 Spring Simulation Conference (SpringSim), pp. 1–12 (2020). https://doi.org/10.22360/SpringSim.2020.CSE.004
2. Cherif, A., Badhib, A., Ammar, H., Alshehri, S., Kalkatawi, M., Imine, A.: Credit card fraud detection in the era of disruptive technologies: a systematic review. J. King Saud Univ. – Comput. Inf. Sci. 35(1), 145–174 (2023). https://doi.org/10.1016/j.jksuci.2022.11.008
3. Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst. Appl. 38(10), 13057–13063 (2011). https://doi.org/10.1016/j.eswa.2011.04.110
4. Gadi, M.F.A., do Lago, A.P., Wang, X.: A comparison of classification methods applied on credit card fraud detection. Technical Report (2016)
5. Goyal, R., Manjhvar, A.K.: Review on credit card fraud detection using data mining classification techniques & machine learning algorithms. SSRN Scholarly Paper 3677692 (2020). https://papers.ssrn.com/abstract=3677692