Supervised Feature Selection Guided by Granular-Ball Computing: A Two-Stage Geometry-Driven Framework

Feature selection is a crucial data preprocessing technique aimed at enhancing robustness against boundary uncertainty and complex geometric distributions and improving model interpretability by removing irrelevant or redundant features. Most filter-based feature selection methods employ a single evaluation metric and often fail to explore the underlying geometric distribution structure of the data, which holds significant guiding information for feature selection. In view of this, this paper proposes a Supervised Feature Selection framework guided by granular-ball computing (SFS-GBC). The framework aims to identify superior feature subset candidates for downstream classification tasks by leveraging geometric insights derived from granular balls. Meanwhile, the proposed algorithm introduces a unique two-stage evaluation process designed to synergize coarse-grained efficiency with fine-grained geometric precision, adapting to data of varying dimensionality. Spectifically First, Granularity Consistency (GC) is employed to rapidly rank all features, effectively pre-screening for candidates highly relevant to the decision classes. More innovatively, the subsequent stage departs from traditional statistical metrics by proposing a novel evaluation measure based on the geometric distribution of granular balls, combining Intra-class Compactness (ICC) and Inter-class Discrimination (ICD) to accurately assess feature subsets within a forward search procedure. Furthermore, by adjusting a granulation parameter, the algorithm can explore the data structure from a multi-granularity perspective, potentially yielding superior feature subsets. Experimental results on public benchmark datasets demonstrate the superiority and effectiveness of the proposed approach.