The Difference Between Precision-recall and ROC Curves for Evaluating the Performance of Credit Card Fraud Detection Models

Rustam Fayzrakhmanov, Alexandr Kulikov, Polina Repp

References:

1. A. C. Bahnsen, D. Aouada, A. Stojanovic, and B. Ottersten, “Feature engineering strategies for credit card fraud detection,” Expert Systems with Applications, vol. 51, pp. 134–142, 2016.
2. Lu Q., Ju C., “Research on credit card fraud detection model based on class weighted support vecto r machine,” Journal of Convergence Information Technology, vol. 6, no. 1, Jan. 2011.
3. Monard, M.C., Batista, G.E., “Learning with skewed class distributions,” Advances in Logic, Artificial Intelligence and Robotics (LAPTEC'02), pp. 173-180, 2002.
4. Zhou, Zhi‐Hua, and Xu‐Ying Liu, " On multi‐class cost‐sensitive learning," Computational Intelligence, vol. 26, no. 3, pp. 232-257, 2010.
5. Dal Pozzolo, Andrea, et al, "Learned lessons in credit card fraud detection from a practitioner perspective," Expert systems with applications, vol .41, no. 10, pp. 4915-4928, 2014.
6. Anis, Maira, Mohsin Ali, and Amit Yadav, "A comparative study of decision tree algorithms for class imbalanced learning in credit card fraud detection," International Journal of Economics, Commerce and Management, vol. 3, no. 12, 2015.
7. Keilwagen, Jens, Ivo Grosse, and Jan Grau, "Area under precision-recall curves for weighted and unweighted data," PLoS One, vol. 9, no. 3, 2014.
8. Novaković, Jasmina Dj, et al, "Evaluation of Classification Models in Machine Learning," Theory and Applications of Mathematics & Computer Science, vol. 1, no. 1, pp. 39-46, 2017.
9. Fawcett, Tom, "An introduction to ROC analysis," Pattern recognition letters, vol. 27, no. 8, pp. 861-874, 2006.
10. Yong, Terence Koon Beh, Chuan Tan Swee, and Theng Yeo Hwee, "Building classification models from imbalanced fraud detection data," Malaysian Journal of Computing, vol. 2, no. 2, pp. 1-21, 2014.
11. King, Gary, and Langche Zeng, "Logistic regression in rare events data," Political analysis, vol. 9, no. 2, pp. 137-163, 2001.
12. Fayzrakhmanov, R. A., Kulikov, A.S., et al, "Prediction of the need for narcotic and psychotropic drugs in the region using random forest model," artificial intelligence in solving actual social and economic problems of the XXI century, pp. 108-111, 2016.
13. Fayzrakhmanov, R. A., et al, "Aplplication of cluster analysis in developing approaches to the selection and designation of treatment regimens for HIV-infected patients," Bulletin of Siberian Medicine, vol. 16, no. 3, pp. 52-60, 2017.
14. Bhattacharyya, Siddhartha, et al, "Data mining for credit card fraud: A comparative study," Decision Support Systems, vol. 50, no.3. pp. 602-613, 2011.
15. Lima, Rafael Franca, and Adriano CM Pereira, "Feature Selection Approaches to Fraud Detection in e-Payment Systems," International Conference on Electronic Commerce and Web Technologies, Springer, Cham, 2016.
16. Davis, Jesse, and Mark Goadrich, "The relationship between Precision-Recall and ROC curves," Proceedings of the 23rd international conference on Machine learning. ACM, 2006.