A voting ensemble machine learning based credit card fraud detection using highly imbalance data
Author:
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/s11042-023-17766-9.pdf
Reference79 articles.
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2. Ahmad H, Kasasbeh B, Aldabaybah B, Rawashdeh E (2023) Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS). Int J Inf Technol. Springer Nature Singapore 15(1):325–33. Available from: https://doi.org/10.1007/s41870-022-00987-w
3. Alejo R, Valdovinos RM, García V, Pacheco-Sanchez JH (2013) A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios. Pattern Recogn Lett 34(4):380–388. https://doi.org/10.1016/j.patrec.2012.09.003
4. Alfaiz NS, Fati SM (2022) Enhanced credit card fraud detection model using machine learning. Electronics 11(4):662. https://doi.org/10.3390/electronics11040662
5. Alghofaili Y, Albattah A, Rassam MA (2020) A financial fraud detection model based on LSTM deep learning technique. J Appl Secur Res. Routledge 15(4):498–516. Available from: https://doi.org/10.1080/19361610.2020.1815491
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