The role of diversity and ensemble learning in credit card fraud detection

Author:

Paldino Gian MarcoORCID,Lebichot Bertrand,Le Borgne Yann-Aël,Siblini Wissam,Oblé Frédéric,Boracchi Giacomo,Bontempi Gianluca

Funder

Innoviris

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Statistics and Probability

Reference73 articles.

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2. Alazizi A, Habrard A, Jacquenet F, He-Guelton L, Oblé F, Siblini W (2019) Anomaly detection, consider your dataset first an illustration on fraud detection. In: 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI). IEEE, pp 1351–1355

3. Alippi C, Boracchi G, Roveri M (2013) Just-in-time classifiers for recurrent concepts. IEEE Trans Neural Netw Learn Syst 24(4):620–634

4. Ba H (2019) Improving detection of credit card fraudulent transactions using generative adversarial networks. arXiv:1907.03355

5. Baesens B (2014) Analytics in a big data world: the essential guide to data science and its applications. Wiley, New York

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