Cost-Sensitive Learning, Simulated PU Learning, and One-Class Autoencoding for Extreme-Imbalance Credit Card Fraud Detection
Extreme class imbalance makes fraud detection evaluation sensitive to both the ranking metric and the chosen operating point. This revised study presents a single-split benchmark on the Credit Card Fraud Detection dataset (284,807 transactions; 492 frauds) comparing cost-sensitive gradient boosting...
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| Asıl Yazarlar: | , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Scientific Publication Center
2024-06-01
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| Seri Bilgileri: | Journal of Advanced Computing Systems |
| Konular: | |
| Online Erişim: | https://scipublication.com/index.php/JACS/article/view/368 |
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