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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Bibliografische Detailangaben

Hauptverfasser:	Jiaying Jin, Tina Huang, Sam Lu
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Scientific Publication Center 2024-06-01
Schriftenreihe:	Journal of Advanced Computing Systems
Schlagworte:	fraud detection extreme class imbalance cost-sensitive learning positive-unlabeled learning focal loss one-class autoencoder
Online-Zugang:	https://scipublication.com/index.php/JACS/article/view/368
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