참고문헌 (38)

1. Expert Syst. Appl. Bahnsen 42 19 6609 2015 10.1016/j.eswa.2015.04.042 Example-dependent cost-sensitive decision trees 

   상세보기

2. Expert Syst. Appl. Bahnsen 51 134 2016 10.1016/j.eswa.2015.12.030 Feature engineering strategies for credit card fraud detection 

   상세보기

3. Decis. Support Syst. Bhattacharyya 50 3 602 2011 10.1016/j.dss.2010.08.008 Data mining for credit card fraud: a comparative study 

   상세보기

4. Ann. Stat. Bühlmann 30 4 927 2002 10.1214/aos/1031689014 Analyzing bagging 

   상세보기

5. Mach. Learn. Breiman 45 1 5 2001 10.1023/A:1010933404324 Random forests 

   

6. Inf. Fusion Carcillo 41 182 2018 10.1016/j.inffus.2017.09.005 Scarff: a scalable framework for streaming credit card fraud detection with spark 

   상세보기

7. Carcillo 631 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017 An assessment of streaming active learning strategies for real-life credit card fraud detection 

8. Int. J. Data Sci. Anal. Carcillo 5 1 2018 Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization 

9. Decis. Support Syst. Carneiro 95 91 2017 10.1016/j.dss.2017.01.002 A data mining based system for credit-card fraud detection in e-tail 

   상세보기

10. ACM Comput. Surv. Chandola 41 3 15 2009 10.1145/1541880.1541882 Anomaly detection: a survey 

    상세보기

11. IEEE Trans. Neural Netw. Learn. Syst. Dal Pozzolo 29 3784 2017 10.1109/TNNLS.2017.2736643 Credit card fraud detection: a realistic modeling and a novel learning strategy 

    상세보기

12. Dal Pozzolo 200 2015 Joint European Conference on Machine Learning and Knowledge Discovery in Databases When is undersampling effective in unbalanced classification tasks? 

13. Expert Syst. Appl. Dal Pozzolo 41 10 4915 2014 10.1016/j.eswa.2014.02.026 Learned lessons in credit card fraud detection from a practitioner perspective 

    상세보기

14. Davis 233 2006 Proceedings of the 23rd International Conference on Machine Learning The relationship between precision-recall and ROC curves 

15. Jpn Soc. Artif. Intell. Freund 14 771-780 1612 1999 A short introduction to boosting 

16. Fu 483 2016 International Conference on Neural Information Processing Credit card fraud detection using convolutional neural networks 

17. Radiology Hanley 143 1 29 1982 10.1148/radiology.143.1.7063747 The meaning and use of the area under a receiver operating characteristic (ROC) curve 

    상세보기

18. J. R. Stat. Soc. Ser. C Hartigan 28 1 100 1979 Algorithm as 136: a k-means clustering algorithm 

19. J. Educ. Psychol. Hotelling 24 6 417 1933 10.1037/h0071325 Analysis of a complex of statistical variables into principal components 

    상세보기

20. Big Data Junqué de Fortuny 1 4 215 2013 10.1089/big.2013.0037 Predictive modeling with big data: is bigger really better? 

    상세보기

21. Expert Syst. Appl. Jurgovsky 100 234 2018 10.1016/j.eswa.2018.01.037 Sequence classification for credit-card fraud detection 

    상세보기

22. Liang 2167 2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Robust contextual outlier detection: where context meets sparsity 

23. Liu 413 2008 Eighth IEEE International Conference on Data Mining Isolation forest 

24. Micenková 51 2014 Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and Description under Data Diversity (ODD2), New York, NY, USA Learning outlier ensembles: the best of both worlds’ supervised and unsupervised 

25. Nguyen 368 2010 International Conference on Database Systems for Advanced Applications Mining outliers with ensemble of heterogeneous detectors on random subspaces 

26. Rayana 1167 2016 2016 IEEE 16th International Conference on Data Mining (ICDM) Sequential ensemble learning for outlier detection: a bias-variance perspective 

27. Int. J. Comput. Sci. Mobile Comput. Sethi 3 4 780 2014 A revived survey of various credit card fraud detection techniques 

28. Int. J. Eng. Comput. Sci. Shimpi 4 11 15010 2015 Survey on credit card fraud detection techniques 

29. IEEE Trans. Knowl. Data Eng. Song 19 5 631 2007 10.1109/TKDE.2007.1009 Conditional anomaly detection 

    상세보기

30. Decis. Support Syst. Van Vlasselaer 75 38 2015 10.1016/j.dss.2015.04.013 Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions 

    상세보기

31. Van Vlasselaer 659 2015 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Afraid: fraud detection via active inference in time-evolving social networks 

32. Veeramachaneni 49 2016 Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS) AI2: training a big data machine to defend 

33. World Wide Web Wei 16 4 449 2013 10.1007/s11280-012-0178-0 Effective detection of sophisticated online banking fraud on extremely imbalanced data 

    상세보기

34. Wold vol. 2 37 1987 Chemometrics and Intelligent Laboratory Systems Principal component analysis 

35. Yamanishi 389 2001 Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner 

36. Procedia Comput. Sci. Zareapoor 48 679 2015 10.1016/j.procs.2015.04.201 Application of credit card fraud detection: based on bagging ensemble classifier 

    상세보기

37. Zhu 2005 Technical Report Semi-Supervised Learning Literature Survey 

38. ACM SIGKDD Explor. Newsl. Zimek 15 1 11 2014 10.1145/2594473.2594476 Ensembles for unsupervised outlier detection: challenges and research questions a position paper 

    상세보기