Paper Info
Title
GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification
Abstract
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 <i>priori</i> 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 <i>a priori</i> 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></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></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 <uri>http://www.cquptshuyinxia.com/GBNRS.html</uri> .
Year
DOI
Venue
2022
10.1109/TKDE.2020.2997039
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Rough sets,neighborhood rough sets,granular ball computing,fuzzy rough sets
Journal
34
Issue
ISSN
Citations 
3
1041-4347
2
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Shuyin Xia120.35
X. Z. Zhang21113.15
Wenhua Li320.35
Guoyin Wang42144202.16
Elisabeth Giem520.35
Zizhong Chen692469.93