Scalable unsupervised labeling with SHAP feature selection for fraud detection in imbalanced data
Abstract There is a growing need for labeled data, yet manual annotation is costly, error-prone, and often infeasible in privacy-sensitive, highly imbalanced domains such as fraud detection. We introduce a fully unsupervised framework that combines unsupervised SHapley Additive exPlanations (SHAP) f...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
SpringerOpen
2025-10-01
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| coleção: | Journal of Big Data |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1186/s40537-025-01248-w |
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