The following publications are possibly variants of this publication:
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- Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud DetectionRichard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin. ictai 2018: 137-142 [doi]
- Medicare Fraud Detection Using Random Forest with Class Imbalanced Big DataRichard A. Bauder, Taghi M. Khoshgoftaar. iri 2018: 80-87 [doi]
- The Effects of Random Undersampling for Big Data Medicare Fraud DetectionJohn T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson. sose 2022: 141-146 [doi]
- Data reduction techniques for highly imbalanced medicare Big DataJohn T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang. jbd, 11(1):8, December 2024. [doi]
- A study on rare fraud predictions with big Medicare claims fraud dataRichard A. Bauder, Taghi M. Khoshgoftaar. ida, 24(1):141-161, 2020. [doi]
- A Survey of Medicare Data Processing and Integration for Fraud DetectionRichard A. Bauder, Taghi M. Khoshgoftaar. iri 2018: 9-14 [doi]
- The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big dataRichard A. Bauder, Taghi M. Khoshgoftaar. hisas, 6(1):9, 2018. [doi]