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

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 3; pp. 1231 - 1242
• Main Authors: Xia, Shuyin, Zhang, Hao, Li, Wenhua, Wang, Guoyin, Giem, Elisabeth, Chen, Zizhong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Approximation algorithms; Big Data; Classification; Complexity; Datasets; Feature extraction; fuzzy rough sets; granular ball computing; Heuristic algorithms; neighborhood rough sets; Optimization; Reduction; Rough set models; Rough sets; Search problems; Source code; Time complexity
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2020.2997039

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 <inline-formula><tex-math notation="LaTeX">O(N^2)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq1-2997039.gif"/> </inline-formula>. To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity <inline-formula><tex-math notation="LaTeX">O(N)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-2997039.gif"/> </inline-formula>, 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 .