Feature selection is a core step in data analysis and is referred to as attribute reduction in rough set theory. Granular ball computing has emerged as a novel data analysis paradigm characterized by high computational efficiency, robustness, and scalability. However, in previous attribute reduction methods for interval numbers, the construction of tolerance classes and the reduction iteration process suffer from inefficiency. To address these limitations, this paper proposes an efficient attribute reduction method based on fuzzy interval-valued granular balls. This method integrates fuzzy interval-valued granular balls with an acceleration strategy based on the positive region. Specifically, we first construct tolerance classes efficiently using fuzzy interval-valued granular balls, thereby enabling a reasonable partition of the universe. We then remove redundant objects in the positive region during the reduction iteration to avoid unnecessary computations. On this basis, we further propose a conditional entropy-based algorithm for attribute reduction. Experimental results show that this algorithm substantially improves computational efficiency while maintaining high classification accuracy.
He et al. (2026) studied this question.