Identification of the Best Combination of Oversampling Technique and Machine Learning Algorithm for Credit Card Fraud Detection
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7962-2_41
Reference15 articles.
1. Lebichot B, Le Borgne Y-A, He L, Oble F, Bontempi G (2019) Deep-learning domain adaptation techniques for credit cards fraud detection, INNSBDDL 2019: recent advances in big data and deep learning, pp 78–88
2. Mohammed E, Far B (2018) Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. IEEE Ann History of Comput IEEE, 1 July 2018. https://doi.org/10.1109/IRI.2018.00025
3. Roy A et al (2018) Deep learning detecting fraud in credit card transactions. In: 2018 Systems and information engineering design symposium (SIEDS). https://doi.org/10.1109/sieds.2018.8374722
4. Awoyemi JO et al (2017) Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 international conference on computing networking and informatics (ICCNI). https://doi.org/10.1109/iccni.2017.8123782
5. Melo-Acosta GE et al (2017) Fraud detection in big data using supervised and semi-supervised learning techniques. In: 2017 IEEE Colombian conference on communications and computing (COLCOM). https://doi.org/10.1109/colcomcon.2017.8088206
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