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Improving Card Fraud Detection Through Suspicious Pattern Discovery

Bibliographic reference Braun, Fabian ; Caelen, Olivier ; Smirnov, Evgueni N. ; Kelk, Steven ; Lebichot, Bertrand. Improving Card Fraud Detection Through Suspicious Pattern Discovery.The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (Arras, du 27/06/2017 au 30/06/2017). In: Advances in Artificial Intelligence: From Theory to Practice (IEA/AIE 2017), Vol. 10351, p. 181-190 (2017)
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  1. Aggarwal, C.C., Han, J.: Frequent Pattern Mining. Springer, Heidelberg (2014)
  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: International Conference on Management of Data (SIGMOD 1993), pp. 207–216. ACM, New York (1993)
  3. Bhattacharyya Siddhartha, Jha Sanjeev, Tharakunnel Kurian, Westland J. Christopher, Data mining for credit card fraud: A comparative study, 10.1016/j.dss.2010.08.008
  4. Bolton Richard J., Hand David J., Provost Foster, Breiman Leo, Bolton Richard J., Hand David J., Statistical Fraud Detection: A ReviewCommentCommentRejoinder, 10.1214/ss/1042727940
  5. Dal Pozzolo, A.: Adaptive machine learning for credit card fraud detection. Ph.D. thesis, Université libre de Bruxelles (2015)
  6. Dal Pozzolo Andrea, Caelen Olivier, Le Borgne Yann-Aël, Waterschoot Serge, Bontempi Gianluca, Learned lessons in credit card fraud detection from a practitioner perspective, 10.1016/j.eswa.2014.02.026
  7. Davis Jesse, Goadrich Mark, The relationship between Precision-Recall and ROC curves, 10.1145/1143844.1143874
  8. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
  9. Jinyan Li, Guimei Liu, Haiquan Li, Limsoon Wong, Maximal Biclique Subgraphs and Closed Pattern Pairs of the Adjacency Matrix: A One-to-One Correspondence and Mining Algorithms, 10.1109/tkde.2007.190660
  10. Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)
  11. Rajaraman Anand, Ullman Jeffrey David, Mining of Massive Datasets, ISBN:9781139058452, 10.1017/cbo9781139058452
  12. Sánchez D., Vila M.A., Cerda L., Serrano J.M., Association rules applied to credit card fraud detection, 10.1016/j.eswa.2008.02.001
  13. Shen Aihua, Tong Rencheng, Deng Yaochen, Application of Classification Models on Credit Card Fraud Detection, 10.1109/icsssm.2007.4280163
  14. Spackman Kent A., SIGNAL DETECTION THEORY: VALUABLE TOOLS FOR EVALUATING INDUCTIVE LEARNING, Proceedings of the Sixth International Workshop on Machine Learning (1989) ISBN:9781558600362 p.160-163, 10.1016/b978-1-55860-036-2.50047-3
  15. Van Hulse Jason, Khoshgoftaar Taghi M., Napolitano Amri, Experimental perspectives on learning from imbalanced data, 10.1145/1273496.1273614
  16. Vlasselaer Veronique Van, Akoglu Leman, Eliassi-Rad Tina, Snoeck Monique, Baesens Bart, Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships, 10.1109/hicss.2015.114
  17. Whitrow C., Hand D. J., Juszczak P., Weston D., Adams N. M., Transaction aggregation as a strategy for credit card fraud detection, 10.1007/s10618-008-0116-z