GBFRS: Robust Fuzzy Rough Sets via Granular Ball Computing

Fuzzy rough set (FRS) theory is effective for handling uncertainty and vagueness in complex datasets. However, most existing models rely on fine-grained pointwise analysis, resulting in poor robustness to noise. This article integrates granular ball computing into FRSs by replacing individual data points with granular balls of varying sizes. A granular ball FRS (GBFRS) framework is proposed and applied to feature selection for the first time. Each granular ball is labeled by the majority class of its internal samples, reducing the impact of noisy instances and improving noise tolerance. Within this framework, a weighted fuzzy dependency function is redefined based on fixed https://github.com/lianxiaoyu724/GBFRS structures, where the weight is determined by the proportion of samples within each granular ball. Larger balls have higher fuzzy dependency values and thus receive greater weights, enabling a more stable evaluation of attribute importance. The theoretical foundations, including properties of lower and upper approximations and the convergence of dependency, are formally established. The experimental results on multiple UCI datasets demonstrate that GBFRS outperforms existing methods in classification accuracy. The source codes and datasets are both available on the public link: https://github.com/lianxiaoyu724/GBFRS.