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2020 – today
- 2023
[j9]John T. Hancock, Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar:
Explainable machine learning models for Medicare fraud detection. J. Big Data 10(1): 154 (2023)- [c17]John T. Hancock, Richard A. Bauder, Taghi M. Khoshgoftaar:
A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers. ICTAI 2023: 92-98 - 2020
- [j8]Richard A. Bauder, Taghi M. Khoshgoftaar:
A study on rare fraud predictions with big Medicare claims fraud data. Intell. Data Anal. 24(1): 141-161 (2020) - [j7]Tawfiq Hasanin
, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder:
Investigating class rarity in big data. J. Big Data 7(1): 23 (2020) - [j6]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
Investigating the relationship between time and predictive model maintenance. J. Big Data 7(1): 36 (2020)
2010 – 2019
- 2019
- [j5]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J. Big Data 6: 21 (2019) - [j4]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder:
Severely imbalanced Big Data challenges: investigating data sampling approaches. J. Big Data 6: 107 (2019) - [c16]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
The Effect of Time on the Maintenance of a Predictive Model. ICMLA 2019: 1891-1896 - [c15]Richard A. Bauder, Matthew Herland, Taghi M. Khoshgoftaar:
Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. IRI 2019: 9-14 - 2018
- [b1]Richard A. Bauder:
Machine Learning Algorithms with Big Medicare Fraud Data. Florida Atlantic University, Boca Raton, USA, 2018 - [j3]Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Inf. Sci. Syst. 6(1): 9 (2018) - [j2]Matthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder:
Big Data fraud detection using multiple medicare data sources. J. Big Data 5: 29 (2018) - [j1]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
A survey on addressing high-class imbalance in big data. J. Big Data 5: 42 (2018) - [c14]Richard A. Bauder, Taghi M. Khoshgoftaar, Amri Napolitano:
Fraud Detection with a Limited Number of Known Fraudulent Medicare Providers. FLAIRS 2018: 299-304 - [c13]Richard A. Bauder, Taghi M. Khoshgoftaar:
The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels. FLAIRS 2018: 404-409 - [c12]Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin:
An Empirical Study on Class Rarity in Big Data. ICMLA 2018: 785-790 - [c11]Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin:
Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection. ICTAI 2018: 137-142 - [c10]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Survey of Medicare Data Processing and Integration for Fraud Detection. IRI 2018: 9-14 - [c9]Richard A. Bauder, Taghi M. Khoshgoftaar:
Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data. IRI 2018: 80-87 - [c8]Richard A. Bauder, Raquel da Rosa, Taghi M. Khoshgoftaar:
Identifying Medicare Provider Fraud with Unsupervised Machine Learning. IRI 2018: 285-292 - 2017
- [c7]Richard A. Bauder, Taghi M. Khoshgoftaar:
Multivariate Anomaly Detection in Medicare using Model Residuals and Probabilistic Programming. FLAIRS 2017: 417-422 - [c6]Richard A. Bauder, Taghi M. Khoshgoftaar:
Medicare Fraud Detection Using Machine Learning Methods. ICMLA 2017: 858-865 - [c5]Richard A. Bauder, Taghi M. Khoshgoftaar:
Estimating Outlier Score Probabilities. IRI 2017: 559-568 - [c4]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims. IRI 2017: 579-588 - 2016
- [c3]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims. ICMLA 2016: 347-354 - [c2]Richard A. Bauder, Taghi M. Khoshgoftaar, Aaron N. Richter, Matthew Herland:
Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims. ICTAI 2016: 784-790 - [c1]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper). IRI 2016: 11-19
Coauthor Index
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