A novel variable-precision granular-ball fuzzy rough set and its application in feature subset selection

Granular-ball computing is an efficient and interpretable theoretical method for multi-granularity data processing. Traditional fuzzy rough set models have some limitations in multi-granularity generation and dynamic characterization. On one hand, most of them lack effective multi-granularity generation methods, and artificially constructed approach may lead to information loss. On the other hand, when the attribute set changes, most fuzzy rough set models exhibit limitations in accurately characterize granularity information changes, and fails to capture correlational changes between attributes. Additionally, these models often lack noise resistance capabilities and flexibility in handling various fuzzy decision scenarios. To overcome these limitations, this paper combines granular-ball computing with fuzzy rough sets, and proposes a new variable-precision granular-ball fuzzy rough set model (VPGBFRS). First, granular-ball fuzzy similarity relations and granular-ball fuzzy neighborhood are used to characterize the relationship between samples. On this basis, a pair of variable-precision granular-ball approximate operators are presented. Second, we construct a variable-precision multi-granularity dependency function to obtain richer classification information, and enhance the model’s ability to capture intrinsic data structures. Finally, we design a forward attribute reduction algorithm based on the variable-precision significance in the sense of remain the classification ability unchanged. Numerical experiments conducted on 12 datasets demonstrate that, compared with four state-of-the-art attribute reduction algorithms, the proposed model exhibits superior performance, achieving significant improvements in both classification accuracy and the size of selected attribute set.