GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification

Feature reduction is an important aspect of Big Data analytics on today's ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than O(N-2). To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity O(N), is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html.

Automated summary

Feature reduction is crucial in Big Data analytics, and rough sets are commonly used for attribute reduction. However, existing rough set algorithms have limitations in terms of efficiency and effectiveness. To address these issues, a novel method called granular ball neighborhood rough sets (GBNRS) is proposed, which outperforms the current state-of-the-art method in terms of performance and classification accuracy.