Imani, M.; Joudaki, M.; Bagheri, A.; Arabnia, H.R.
Why ROC-AUC Is Misleading for Highly Imbalanced Data: In-Depth Evaluation of MCC, F2-Score, H-Measure, and AUC-Based Metrics Across Diverse Classifiers. Technologies 2026, 14, 54.
https://doi.org/10.3390/technologies14010054
AMA Style
Imani M, Joudaki M, Bagheri A, Arabnia HR.
Why ROC-AUC Is Misleading for Highly Imbalanced Data: In-Depth Evaluation of MCC, F2-Score, H-Measure, and AUC-Based Metrics Across Diverse Classifiers. Technologies. 2026; 14(1):54.
https://doi.org/10.3390/technologies14010054
Chicago/Turabian Style
Imani, Mehdi, Majid Joudaki, Ayoub Bagheri, and Hamid R. Arabnia.
2026. "Why ROC-AUC Is Misleading for Highly Imbalanced Data: In-Depth Evaluation of MCC, F2-Score, H-Measure, and AUC-Based Metrics Across Diverse Classifiers" Technologies 14, no. 1: 54.
https://doi.org/10.3390/technologies14010054
APA Style
Imani, M., Joudaki, M., Bagheri, A., & Arabnia, H. R.
(2026). Why ROC-AUC Is Misleading for Highly Imbalanced Data: In-Depth Evaluation of MCC, F2-Score, H-Measure, and AUC-Based Metrics Across Diverse Classifiers. Technologies, 14(1), 54.
https://doi.org/10.3390/technologies14010054