Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks
The high prevalence of fraud in contemporary financial transactions necessitates advanced anomaly detection systems to address the significant imbalance between legitimate and anomalous transactions in real-time datasets. To address this issue, our study introduces a novel approach, the regularized...
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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2025-01-01
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Series: | Alexandria Engineering Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012523 |
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