The following publications are possibly variants of this publication:
- Machine Learning Algorithms with Big Medicare Fraud DataRichard A. Bauder. PhD thesis, Florida Atlantic University, Boca Raton, USA, 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]
- Medicare Fraud Detection Using Random Forest with Class Imbalanced Big DataRichard A. Bauder, Taghi M. Khoshgoftaar. iri 2018: 80-87 [doi]
- Hyperparameter Tuning for Medicare Fraud Detection in Big DataJohn T. Hancock, Taghi M. Khoshgoftaar. sncs, 3(6):440, 2022. [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]
- 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 for Big Data Medicare Fraud DetectionJohn T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson. sose 2022: 141-146 [doi]