{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:52:15Z","timestamp":1778860335859,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004744","name":"Innoviris","doi-asserted-by":"publisher","award":["2017-R-49a"],"award-info":[{"award-number":["2017-R-49a"]}],"id":[{"id":"10.13039\/501100004744","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s41060-021-00258-0","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T17:02:42Z","timestamp":1623258162000},"page":"165-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Incremental learning strategies for credit cards fraud detection"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2188-0118","authenticated-orcid":false,"given":"B.","family":"Lebichot","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. M.","family":"Paldino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"W.","family":"Siblini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L.","family":"He-Guelton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Obl\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Bontempi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"258_CR1","unstructured":"HSN Consultants, Inc, The nilson report 2019 (consulted on 2020-03-17). https:\/\/nilsonreport.com"},{"key":"258_CR2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jnca.2016.04.007","volume":"68","author":"A Abdallah","year":"2016","unstructured":"Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system?: A survey. J. Netw. Comput. Appl. 68, 90\u2013113 (2016)","journal-title":"J. Netw. Comput. Appl."},{"issue":"3","key":"258_CR3","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.dss.2010.08.008","volume":"50","author":"S Bhattacharyya","year":"2011","unstructured":"Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: A comparative study. Decis. Support Syst. 50(3), 602\u2013613 (2011)","journal-title":"Decis. Support Syst."},{"issue":"1","key":"258_CR4","first-page":"69","volume":"23","author":"G Widmer","year":"1996","unstructured":"Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. learn. 23(1), 69\u2013101 (1996)","journal-title":"Mach. learn."},{"key":"258_CR5","unstructured":"Zinkevich, M.: Online convex programming and generalized infinitesimal gradient ascent, In: Proceedings of the 20th international conference on machine learning (icml-03), pp. 928\u2013936 (2003)"},{"issue":"2\u20133","key":"258_CR6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10994-007-5016-8","volume":"69","author":"E Hazan","year":"2007","unstructured":"Hazan, E., Agarwal, A., Kale, S.: Logarithmic regret algorithms for online convex optimization. Mach. Learn. 69(2\u20133), 169\u2013192 (2007)","journal-title":"Mach. Learn."},{"issue":"41","key":"258_CR7","doi-asserted-by":"publisher","first-page":"4915","DOI":"10.1016\/j.eswa.2014.02.026","volume":"10","author":"A Dal Pozzolo","year":"2014","unstructured":"Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., Bontempi, G.: Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl. 10(41), 4915\u20134928 (2014)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"258_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.inffus.2004.04.004","volume":"6","author":"G Brown","year":"2005","unstructured":"Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1), 5\u201320 (2005)","journal-title":"Inf. Fusion"},{"issue":"10","key":"258_CR9","doi-asserted-by":"publisher","first-page":"4822","DOI":"10.1109\/TNNLS.2017.2775225","volume":"29","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Tang, K., Zhu, Z., Yao, X.: Concept drift adaptation by exploiting historical knowledge. IEEE Trans. Neural Netw. Learn. Syst 29(10), 4822\u20134832 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"issue":"10","key":"258_CR10","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"258_CR11","doi-asserted-by":"crossref","unstructured":"W.\u00a0N. Street, Y.\u00a0Kim, A streaming ensemble algorithm (sea) for large-scale classification, In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001, pp. 377\u2013382","DOI":"10.1145\/502512.502568"},{"issue":"10","key":"258_CR12","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","volume":"22","author":"R Elwell","year":"2011","unstructured":"Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517\u20131531 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"key":"258_CR13","doi-asserted-by":"crossref","unstructured":"S.\u00a0Ghosh, D.\u00a0L. Reilly, Credit card fraud detection with a neural-network, in: System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on, Vol.\u00a03, IEEE, 1994, pp. 621\u2013630","DOI":"10.1109\/HICSS.1994.323314"},{"issue":"4","key":"258_CR14","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1109\/72.595879","volume":"8","author":"JR Dorronsoro","year":"1997","unstructured":"Dorronsoro, J.R., Ginel, F., Sgnchez, C., Cruz, C.S.: Neural fraud detection in credit card operations. IEEE Trans. Neural Netw. 8(4), 827\u2013834 (1997)","journal-title":"IEEE Trans. Neural Netw."},{"key":"258_CR15","doi-asserted-by":"crossref","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: A review. Neural Netw. (2019)","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"258_CR16","doi-asserted-by":"crossref","unstructured":"Fu, K., Cheng, D., Tu, Y., Zhang, L.: Credit card fraud detection using convolutional neural networks, In: International Conference on Neural Information Processing, Springer, pp. 483\u2013490 (2016)","DOI":"10.1007\/978-3-319-46675-0_53"},{"issue":"1","key":"258_CR17","first-page":"18","volume":"9","author":"A Pumsirirat","year":"2018","unstructured":"Pumsirirat, A., Yan, L.: Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. Int. J. Adv. Comput. Sci. Appl. 9(1), 18\u201325 (2018)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"258_CR18","doi-asserted-by":"crossref","unstructured":"Abakarim, Y., Lahby, M., Attioui, A.: An efficient real time model for credit card fraud detection based on deep learning, In: Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications, pp. 1\u20137 (2018)","DOI":"10.1145\/3289402.3289530"},{"key":"258_CR19","unstructured":"Nguyen, T.T., Tahir, H., Abdelrazek, M., Babar, A.: Deep learning methods for credit card fraud detection, arXiv preprint arXiv:2012.03754 (2020)"},{"key":"258_CR20","doi-asserted-by":"crossref","unstructured":"Najadat, H., Altiti, O., Aqouleh, A.A., Younes, M.: Credit card fraud detection based on machine and deep learning, In: 2020 11th International Conference on Information and Communication Systems (ICICS), IEEE, pp. 204\u2013208 (2020)","DOI":"10.1109\/ICICS49469.2020.239524"},{"key":"258_CR21","doi-asserted-by":"crossref","unstructured":"Forough, J., Momtazi, S.: Ensemble of deep sequential models for credit card fraud detection. Appl. Soft Comput. 99, (2021)","DOI":"10.1016\/j.asoc.2020.106883"},{"issue":"4","key":"258_CR22","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1109\/TNNLS.2013.2239309","volume":"24","author":"C Alippi","year":"2013","unstructured":"Alippi, C., Boracchi, G., Roveri, M.: Just-in-time classifiers for recurrent concepts. IEEE Trans. Neural Netw. Learn. Syst. 24(4), 620\u2013634 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"258_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama, J., \u017dliobait\u0117, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 1\u201337 (2014)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"258_CR24","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.inffus.2017.09.005","volume":"41","author":"F Carcillo","year":"2018","unstructured":"Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., Bontempi, G.: Scarff: a scalable framework for streaming credit card fraud detection with spark. Inf. Fusion 41, 182\u2013194 (2018)","journal-title":"Inf. Fusion"},{"key":"258_CR25","doi-asserted-by":"crossref","unstructured":"Saito, T., Rehmsmeier, M.: The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PloS one 10(3), (2015)","DOI":"10.1371\/journal.pone.0118432"},{"key":"258_CR26","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves, In: Proceedings of the 23rd international conference on Machine learning, pp. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"258_CR27","first-page":"721","volume-title":"A graph-based, semi-supervised, credit card fraud detection system","author":"B Lebichot","year":"2017","unstructured":"Lebichot, B., Braun, F., Caelen, O., Saerens, M.: A graph-based, semi-supervised, credit card fraud detection system, pp. 721\u2013733. Springer, Cham (2017)"},{"key":"258_CR28","unstructured":"Dal Pozzolo, A.: Adaptive machine learning for credit card fraud detection, Ph.D. thesis, Universite Libre de Bruxelles (2015)"},{"key":"258_CR29","unstructured":"Machine Learning Group - ULB, Credit card fraud detection (consulted on 2020-06-28). https:\/\/www.kaggle.com\/mlg-ulb\/creditcardfraud"},{"key":"258_CR30","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.eswa.2018.01.037","volume":"100","author":"J Jurgovsky","year":"2018","unstructured":"Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He, L., Caelen, O.: Sequence classification for credit-card fraud detection. Expert Syst. Appl. 100, 234\u2013245 (2018)","journal-title":"Expert Syst. Appl."},{"key":"258_CR31","unstructured":"Chollet, F.: et\u00a0al., Keras, https:\/\/keras.io (2015)"},{"key":"258_CR32","doi-asserted-by":"crossref","unstructured":"R.\u00a0Chalapathy, S.\u00a0Chawla, Deep learning for anomaly detection: A survey, arXiv preprint arXiv:1901.03407 (2019)","DOI":"10.1145\/3394486.3406704"},{"key":"258_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection, In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"258_CR34","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.-Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"258_CR35","unstructured":"Japkowicz, N.: Learning from imbalanced data sets: a comparison of various strategies, In AAAI Workshop on Learning from Imbalanced Data Sets (2000)"},{"key":"258_CR36","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ins.2019.05.042","volume":"557","author":"F Carcillo","year":"2019","unstructured":"Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Obl\u00e9, F., Bontempi, G.: Combining unsupervised and supervised learning in credit card fraud detection. Inf. Sci. 557, 317\u2013331 (2019)","journal-title":"Inf. Sci."},{"key":"258_CR37","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar, J.: Statistical comparaison of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"258_CR38","unstructured":"B.\u00a0Lebichot, T.\u00a0Verhelst, Y.-A. Le\u00a0Borgne, L.\u00a0He-Guelton, F.\u00a0Obl\u00e9, G.\u00a0Bontempi, Transfer learning strategies for credit card fraud detection (submitted for publication)"},{"key":"258_CR39","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-16841-4_8","volume-title":"Recent Advances in Big Data and Deep Learning","author":"B Lebichot","year":"2020","unstructured":"Lebichot, B., Le Borgne, Y.-A., He-Guelton, L., Obl\u00e9, F., Bontempi, G.: Deep-learning domain adaptation techniques for credit cards fraud detection. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds.) Recent Advances in Big Data and Deep Learning, pp. 78\u201388. Springer International Publishing, Cham (2020)"},{"key":"258_CR40","doi-asserted-by":"crossref","unstructured":"Huang, J., Smola, A.J., Gretton, A., Borgwardt, K.M., Scholkopf, B.: Correcting sample selection bias by unlabeled data, In: Proceedings of the 19th International Conference on Neural Information Processing Systems, NIPS\u201906, MIT Press, pp. 601\u2013608 (2006)","DOI":"10.7551\/mitpress\/7503.003.0080"},{"issue":"10","key":"258_CR41","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1016\/S0893-6080(99)00073-8","volume":"12","author":"Y Liu","year":"1999","unstructured":"Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Netw. 12(10), 1399\u20131404 (1999)","journal-title":"Neural Netw."},{"key":"258_CR42","doi-asserted-by":"crossref","unstructured":"Siblini, W., Fr\u00e9ry, J., He-Guelton, L., Obl\u00e9, F., Wang, Y.-Q.: Master your metrics with calibration, In: International Symposium on Intelligent Data Analysis, Springer, pp. 457\u2013469 (2020)","DOI":"10.1007\/978-3-030-44584-3_36"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-021-00258-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-021-00258-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-021-00258-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T18:27:42Z","timestamp":1699122462000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-021-00258-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,9]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["258"],"URL":"https:\/\/doi.org\/10.1007\/s41060-021-00258-0","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,9]]},"assertion":[{"value":"4 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare they have no conflicts of interest or competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"The main dataset cannot be made available for confidential reasons. A good proxy is the public Kaggle dataset [], a two-day long, anonymized extract from the same process. Experimental results with the public dataset are reported in Sect..","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}},{"value":"Code cannot be made available for confidential reasons.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}