The following publications are possibly variants of this publication:
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- 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]
- Big Data fraud detection using multiple medicare data sourcesMatthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder. jbd, 5:29, 2018. [doi]
- Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud DetectionRichard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin. ictai 2018: 137-142 [doi]
- Improving Medicare Fraud Detection through Big Data Size Reduction TechniquesHuanjing Wang, John T. Hancock, Taghi M. Khoshgoftaar. sose 2023: 208-217 [doi]
- The Effects of Random Undersampling with Simulated Class Imbalance for Big DataTawfiq Hasanin, Taghi M. Khoshgoftaar. iri 2018: 70-79 [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]