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by S Xiang · 2023 · Cited by 113 — To compare the capability of semi-supervised learning, we further set the ratio of the training set to the whole dataset to different values.!by W Zheng · Cited by 182 — We consider the problem of fraudulent credit card detec- tion as meta-learning- and federated-learning-based (feder- ated meta-learning based) classi~by Y Tang · 2024 · Cited by 9 — The training process of credit card fraud in multi-bank scenario based on federated learning is shown in Fig. 2, assuming that there are N *14*by A Khadka · 2025 — Their approach involves training two separate classifiers, one on recent feedbacks and another on delayed labels, and combining them to enhance precision.&May 3, 2025 — Four subscription plans are now simplified to three. FEM and FSLM subscriptions are now priced the same. Facilities can now get a 15% discount applied to multi *.by D Saleh · 2025 — In this study, we aim to explore what effect modifying the training scheme of a proven hybrid model in credit card fraud detection can have any *41 pages-by W Yang · Cited by 333 — Different from the traditional FDS trained with data centralized in the cloud, FFD enables banks to learn fraud detection model with the training data