Data reduction techniques for highly imbalanced medicare Big Data
Abstract In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge. This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), and a novel ensemble supervised feature selectio...
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| Hlavní autoři: | , , , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
SpringerOpen
2024-01-01
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| Edice: | Journal of Big Data |
| Témata: | |
| On-line přístup: | https://doi.org/10.1186/s40537-023-00869-3 |
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