An Efficient Feature Selection Method Using the Granular-Ball Divergence-Based Fuzzy Rough Hypergraph
Granular-Ball Computing (GBC) is an efficient, robust, and highly interpretable multi-granularity representation and computation method. Nonetheless, most feature selection methods based on GBC require considerable time to calculate the significance measures of features or repeatedly generate granular balls, which limits their applicability to high-dimensional data. The graph-based feature selection effectively reduces dimensionality by exploiting feature correlations and redundancies. However, most graph-based feature selection methods are limited to a fine and single granularity knowledge space. Driven by these issues, this paper first proposes the granular-ball divergence-based fuzzy rough set to characterize the uncertain information from a multi-granularity perspective. Then, the minimum discriminative criterion for constructing a hypergraph is evaluated by the approximation operators, and the correlations between theories are established. On this basis, the importance of features is defined as the weights of hypernodes, and the strategies of Important Retaining (IR) and Redundant Pruning (RP) are designed to select the most important feature and improve execution efficiency, respectively, which is equivalent to an iterative weighted maximum coverage problem with dynamic weight updates. Finally, a feature selection algorithm is designed to select the best feature subset. The experimental results show that our algorithm achieves better classification performance and higher execution efficiency.