Data reduction techniques for highly imbalanced medicare Big Data

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
Main Authors: John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Journal of Big Data
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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