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Informative Evaluation Metrics for Highly Imbalanced Big Data Classification.

, , and . ICMLA, page 1419-1426. IEEE, (2022)

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A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches., , , and . CogMI, page 75-82. IEEE, (2023)Improving Medicare Fraud Detection through Big Data Size Reduction Techniques., , and . SOSE, page 208-217. IEEE, (2023)Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud., , and . IRI, page 267-272. IEEE, (2023)Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection., and . IRI, page 243-249. IEEE, (2022)A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data., , and . CIC, page 135-142. IEEE, (2022)Informative Evaluation Metrics for Highly Imbalanced Big Data Classification., , and . ICMLA, page 1419-1426. IEEE, (2022)Performance of CatBoost and XGBoost in Medicare Fraud Detection., and . ICMLA, page 572-579. IEEE, (2020)Explainable machine learning models for Medicare fraud detection., , , and . J. Big Data, 10 (1): 154 (December 2023)Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods., , , and . J. Big Data, 11 (1): 44 (December 2024)Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection., and . SN Comput. Sci., 2 (4): 268 (2021)