MAGA: A Controlled Minority-Class Generative-Augmentation Framework With Fairness-Aware Selection for Imbalanced Diabetes Tabular Classification

Imbalanced tabular classification remains a central challenge in clinical risk modeling, where minority-class misses can be costly and subgroup performance disparities are undesirable. This study evaluates augmentation for diabetes-focused classification with six classical oversamplers (SMOTE, ADASY...

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Bibliografische Detailangaben

Hauptverfasser:	Nikos Fazakis, Sotiris Kotsiantis, Yiannis Dimakopoulos
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2026-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Class imbalance diabetes prediction tabular data augmentation fairness equal opportunity demographic parity
Online-Zugang:	https://ieeexplore.ieee.org/document/11503261/
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