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English Information

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Authors
# Name
1 Arthur Cavalcanti(arthur.silveira@aluno.cefet-rj.br)
2 Diego Brandão(diego.brandao@cefet-rj.br)
3 Eduardo Bezerra(ebezerra@cefet-rj.br)
4 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)

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Reference
# Reference
1 Amit Singh, R. K. R. and Tiwari, A. (2022). Credit card fraud detection under extreme imbalanced data: A comparative study of data-level algorithms. Journal of Experimental & Theoretical Artificial Intelligence, 34(4):571–598.
2 Bhagwani, H., Agarwal, S., Kodipalli, A., and Martis, R. J. (2021). Targeting class imbalance problem using gan. In 5th Inter. Conf. on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), pages 318–322.
3 Bhattacharyya, S. et al. (2011). Data mining for credit card fraud: A comparative study. Decis. Support Syst., 50:602–613.
4 Carcillo, F. et al. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557:317–331.
5 Ghaleb, F. A. et al. (2023). Ensemble synthesized minority oversampling-based generative adversarial networks and random forest algorithm for credit card fraud detection. IEEE Access, 11:89694–89710.
6 Gupta, P., Varshney, A., Khan, M. R., Ahmed, R., Shuaib, M., and Alam, S. (2023). Unbalanced credit card fraud detection data: A machine learning-oriented comparative study of balancing techniques. Procedia Computer Science, 218:2575–2584. International Conference on Machine Learning and Data Engineering.
7 Hasib, K. M. et al. (2020). A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem. Journal of Computer Science, 16(11), 1546-1557.
8 He, H. and Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263–1284.
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10 Ileberi, E. et al. (2021). Performance evaluation of machine learning methods for credit card fraud detection using smote and adaboost. IEEE Access, 9:165286–165294.
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15 Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.-S., and Zeineddine, H. (2019). An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7:93010–93022.
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17 Prabha, D. P. and Priscilla, C. V. (2024). Estimation of optimal threshold shifting to handle class imbalance in credit card fraud detection using machine learning techniques. In American Institute of Physics Conference Series, volume 2802, page 120014. AIP.
18 Priscilla, C. V. and Prabha, D. P. (2020). Influence of optimizing xgboost to handle class imbalance in credit card fraud detection. In 3rd Inter. Conf. on Smart Systems and Inventive Technology (ICSSIT), page 1309–1315.
19 Sisodia, D. S., Reddy, N. K., and Bhandari, S. (2017). Performance evaluation of class balancing techniques for credit card fraud detection. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pages 2747–2752.
20 Sun, Y. et al. (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 23(04):687–719.
21 Xie, Y., Li, A., Gao, L., and Liu, Z. (2021). A heterogeneous ensemble learning model based on data distribution for credit card fraud detection. Wireless Communications and Mobile Computing, 2021:1–13.
22 Zhang, F., Liu, G., Li, Z., Yan, C., and Jiang, C. (2019). Gmm-based undersampling and its application for credit card fraud detection. In International Joint Conference on Neural Networks, pages 1–8.