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GAN-based Data Augmentation for Credit Card Fraud Detection.

Strelcenia, E and Prakoonwit, S, 2023. GAN-based Data Augmentation for Credit Card Fraud Detection. In: 2022 IEEE International Conference on Big Data (Big Data). New York, NY: USA: IEEE, 6812-6814.

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GAN-based Data Augmentation for Credit Card Fraud Detection.pdf - Accepted Version
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DOI: 10.1109/BigData55660.2022.10020419


Deep generative approaches, such as GANs (generative adversarial networks), can be used to efficiently generate new data points that are similar to existing ones. This can be useful for increasing the size of a dataset or for creating synthetic data points that can be used in place of real ones. In this study, we trained classifiers using our novel K-CGAN approach and compared them to other oversampling approaches. We achieved higher F1 score performance metrics than the other methods. After conducting several experiments, we found that classifiers based on a Random Forest, Nearest Neighbor, Logistic Regression, MLP or Adaboost algorithm trained on the augmented set performed much better than those trained on the original data. This effectively creates a fraud detection mechanism.

Item Type:Book Section
Group:Faculty of Science & Technology
ID Code:38333
Deposited By: Symplectic RT2
Deposited On:07 Aug 2023 12:49
Last Modified:07 Aug 2023 12:50


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