Avaliação empírica de classificadores e métodos de balanceamento para detecção de fraudes em transações com cartões de créditos
Resumo
Algoritmos de aprendizado de máquina são amplamente utilizados em sistemas para detecção de fraudes em cartões de crédito devido à capacidade de distinguir entre transações legítimas e fraudulentas. Um problema reconhecido nesta área é o alto desbalanceamento usualmente encontrado nas classes, que pode comprometer o desempenho dos classificadores. Os estudos empíricos encontrados na literatura aplicam, no máximo, duas técnicas de amostragem. Este artigo traz um estudo comparativo de cinco modelos de classificação sob cinco diferentes métodos de balanceamento dos conjuntos de treinamento. O melhor desempenho foi obtido pela random forest, que além de ter apresentado o maior F-score médio (0,867), mostrou-se consideravelmente mais robusta do que os demais classificadores em relação à escolha da técnica de balanceamento e à seleção de atributos.
Palavras-chave:
Detecção de Fraudes, Cartão de Crédito, Balanceamento, Aprendizado Supervisionado, Random Forest
Referências
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Awoyemi, J. O., Adetunmbi, A. O., and Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. In 2017 International Conference on Computing Networking and Informatics (ICCNI), pages 1–9.
Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl., 6(1):20–29.
Bowyer, K. W., Chawla, N. V., Hall, L. O., and Kegelmeyer, W. P. (2011). SMOTE: synthetic minority over-sampling technique. CoRR, abs/1106.1813.
Breiman, L. (2001). Random forests. Mach. Learn., 45(1):5–32.
Chan, P. K., Fan, W., Prodromidis, A. L., and Stolfo, S. J. (1999). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems and their Applications, 14(6):67–74.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Mach. Learn., 20(3):273–297.
Dhankhad, S., Mohammed, E., and Far, B. (2018). Supervised machine learning algorithms for credit card fraudulent transaction detection: A comparative study. In 2018 IEEE International Conference on Information Reuse and Integration (IRI), pages 122–125.
Dupret, G. and Koda, M. (2001). Bootstrap re-sampling for unbalanced data in supervised learning. European Journal of Operational Research, 134(1):141 – 156.
Hesterberg, T., Monaghan, S., Moore, D., Clipson, A., and Epstein, R. (2003). Bootstrap Methods and Permutation Tests: Companion Chapter 18 to the Practice of Business Statistics. W.H.Freeman and Company, New York.
Khatri, S., Arora, A., and Agrawal, A. P. (2020). Supervised machine learning algorithms for credit card fraud detection: A comparison. In 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence), pages 680–683.
Kuhn, M. and Johnson, K. (2013). Applied Predictive Learning. Springer, New York, NY, USA.
Maron, M. E. (1961). Automatic indexing: An experimental inquiry. J. ACM, 8(3):404–417.
Mishra, A. and Ghorpade, C. (2018). Credit card fraud detection on the skewed data using various classification and ensemble techniques. In 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pages 1–5.
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996). Applied Linear Statistical Models. Irwin.
Niu, X., Wang, L., and Yang, X. (2019). A comparison study of credit card fraud detection: Supervised versus unsupervised. CoRR, abs/1904.10604.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ren, H. and Yang, B. (2019). Clustering-based prototype generation for imbalance classification. In 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), pages 422–426.
Sahin, Y. and Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011, 1:442–447.
Schmidt, M., Le Roux, N., and Bach, F. (2017). Minimizing finite sums with the stochastic average gradient. Math. Program., 162(1–2):83–112.
Tomek, I. (1976). Two modifications of cnn. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(11):769–772.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., and Anderla, A. (2019). Credit card fraud detection - machine learning methods. pages 1–5.
Awoyemi, J. O., Adetunmbi, A. O., and Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. In 2017 International Conference on Computing Networking and Informatics (ICCNI), pages 1–9.
Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl., 6(1):20–29.
Bowyer, K. W., Chawla, N. V., Hall, L. O., and Kegelmeyer, W. P. (2011). SMOTE: synthetic minority over-sampling technique. CoRR, abs/1106.1813.
Breiman, L. (2001). Random forests. Mach. Learn., 45(1):5–32.
Chan, P. K., Fan, W., Prodromidis, A. L., and Stolfo, S. J. (1999). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems and their Applications, 14(6):67–74.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Mach. Learn., 20(3):273–297.
Dhankhad, S., Mohammed, E., and Far, B. (2018). Supervised machine learning algorithms for credit card fraudulent transaction detection: A comparative study. In 2018 IEEE International Conference on Information Reuse and Integration (IRI), pages 122–125.
Dupret, G. and Koda, M. (2001). Bootstrap re-sampling for unbalanced data in supervised learning. European Journal of Operational Research, 134(1):141 – 156.
Hesterberg, T., Monaghan, S., Moore, D., Clipson, A., and Epstein, R. (2003). Bootstrap Methods and Permutation Tests: Companion Chapter 18 to the Practice of Business Statistics. W.H.Freeman and Company, New York.
Khatri, S., Arora, A., and Agrawal, A. P. (2020). Supervised machine learning algorithms for credit card fraud detection: A comparison. In 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence), pages 680–683.
Kuhn, M. and Johnson, K. (2013). Applied Predictive Learning. Springer, New York, NY, USA.
Maron, M. E. (1961). Automatic indexing: An experimental inquiry. J. ACM, 8(3):404–417.
Mishra, A. and Ghorpade, C. (2018). Credit card fraud detection on the skewed data using various classification and ensemble techniques. In 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pages 1–5.
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996). Applied Linear Statistical Models. Irwin.
Niu, X., Wang, L., and Yang, X. (2019). A comparison study of credit card fraud detection: Supervised versus unsupervised. CoRR, abs/1904.10604.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ren, H. and Yang, B. (2019). Clustering-based prototype generation for imbalance classification. In 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), pages 422–426.
Sahin, Y. and Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011, 1:442–447.
Schmidt, M., Le Roux, N., and Bach, F. (2017). Minimizing finite sums with the stochastic average gradient. Math. Program., 162(1–2):83–112.
Tomek, I. (1976). Two modifications of cnn. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(11):769–772.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., and Anderla, A. (2019). Credit card fraud detection - machine learning methods. pages 1–5.
Publicado
20/10/2020
Como Citar
NICOLA, Victor; LAURETTO, Marcelo; VALDIVIA DELGADO, Karina.
Avaliação empírica de classificadores e métodos de balanceamento para detecção de fraudes em transações com cartões de créditos. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 70-81.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12118.
