Credit card fraud detection using ensemble data mining methods-Reference-Cited by-同舟云学术

Credit card fraud detection using ensemble data mining methods

Published:2023-03-09 Issue:19 Volume:82 Page:29057-29075
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Bakhtiari Saeid^ORCID,Nasiri Zahra^ORCID,Vahidi Javad^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-14698-2.pdf

Reference31 articles.

1. Abdulrazaq AA, Abdulrazaq MB, Umoh IJ, Adedokun EA (2019) "Fraud detection in credit card and application of VAT clustering algorithm: a review," in 2019 2nd international conference of the IEEE Nigeria computer chapter (NigeriaComputConf)

2. Alicja G, Bakala M, Woznica K, Zwolinski M, Biecek P (2019) "EPP: interpretable score of model predictive power.," arXiv, p. preprint arXiv:1908.09213

3. Altyeb Altaher T, Malebary SJ (2020) An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine. IEEE Access 8 8:25579–25587

4. Arya M, Hanumat SG (2020) DEAL–‘deep ensemble ALgorithm’Framework for credit card fraud detection in real-time data stream with Google TensorFlow. Smart Sci 8(2):71–83

5. Ayyadevara VK (2018) "Gradient Boosting Machine," Pro Machine Learning Algorithms. pp. 117–134

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