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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Bibliographic Details
Published in:Journal of Big Data
Main Authors: John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Subjects:
Random undersampling
Ensemble supervised feature selection
Big Data
Medicare fraud detection
Online Access:https://doi.org/10.1186/s40537-023-00869-3

Internet

https://doi.org/10.1186/s40537-023-00869-3

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