FairGB: A Fair Granular-Ball Generation Method for Data Classification

With the widespread application of data-driven classifiers in high-risk domains, group fairness has become an important research focus. However, most existing methods rely on model constraints or data reweighting, which may limit interpretability or distort the original data distribution. Granular-ball computing (GBC), as a structured and interpretable learning framework, provides a natural foundation for incorporating group fairness into data partitioning. Based on this insight, we propose a $\textbf{Fair}$ $\textbf{G}$ranular-$\textbf{B}$all $\textbf{G}$eneration framework (FairGBG), which employs fair clustering to maintain balanced proportions of sensitive groups within each granular ball (GB), thereby enhancing within-ball group fairness. Theoretical analysis shows that FairGBG can preserve high GB purity while satisfying group fairness requirements. Furthermore, we introduce a $\textbf{Fair}$ $\textbf{G}$ranular-$\textbf{B}$all-based data $\textbf{F}$air $\textbf{C}$lassification method (FairGBFC), which leverages fair GBs to improve classification fairness. Experiments on multiple benchmark datasets demonstrate that, compared with existing GB generation methods, FairGBG can generate locally fair GBs. Moreover, compared with state-of-the-art fairness-aware baselines, FairGBFC achieves a superior trade-off between accuracy and fairness, effectively mitigating bias while preserving high utility. Our code is publicly available at https://github.com/jamesandai/FairGB.