A Credit Card Fraud Detection Model Using Machine Learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced Datasets for High Scores-Reference-Cited by-同舟云学术

A Credit Card Fraud Detection Model Using Machine Learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced Datasets for High Scores

Published:2023 Issue: Volume: Page:142-155
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Sinayobye Omar^ORCID,Musabe Richard^ORCID,Uwitonze Alfred^ORCID,Ngenzi Alexander^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-34222-6_12

Reference34 articles.

1. Nadim, A.H., et al.: Analysis of machine learning techniques for credit card fraud detection. In: 2019 International Conference on Machine Learning and Data Engineering (iCMLDE). IEEE (2019). https://doi.org/10.1109/iCMLDE49015.2019.00019

2. Dal Pozzolo, A., et al.: Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3784–3797 (2017). https://doi.org/10.1109/TNNLS.2017.2736643

3. Awoyemi, J.O., Adetunmbi, A.O., Oluwadare, S.A.: Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 International Conference on Computing Networking and Informatics (ICCNI). IEEE (2017). https://doi.org/10.1109/ICCNI.2017.8123782

4. Dal Pozzolo, A.: Adaptive machine learning for credit card fraud detection. Unpublished doctoral dissertation, Université libre de Bruxelles, Faculte des Sciences—Informatique, Bruxelles (2015)

5. Advances in Intelligent Systems and Computing;N Rout,2018

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhanced autoencoder-based fraud detection: a novel approach with noise factor encoding and SMOTE;Knowledge and Information Systems;2023-11-27