Attribute reduction based on a rapid variable granular ball generation model

Abstract

Attribute reduction is a key step in processing large-scale datasets, where the Granular Ball Neighborhood Rough Set (GBNRS) can significantly enhance the performance of attribute reduction compared to the traditional Neighborhood Rough Set (NRS). However, the GBNRS algorithm faces such challenges as a sharp increase in computational costs in high-dimensional spaces. To address these issues, this study introduces a new granular ball quality index to judge the separability degree of decision classes, and on the basis of this index, a rapid variable granular ball generation model (RVGBGM) is proposed. Compared with GBNRS, RVGBGM has the following advantages: 1) it reduces the number of granular balls and can quickly reflect the separability degree of different decision classes with few granular balls, 2) it constructs granular balls by using label information and shortens the time of granular ball construction, and 3) it can adjust the radius of granular balls adaptively by using parameters to determine the optimal granular ball radius for different datasets. Finally, we compare the RVGBGM algorithm with classical attribute reduction algorithms and the current state-of-the-art granular ball algorithm on 11 datasets. The proposed algorithm significantly improves algorithm efficiency while maintaining high accuracy.