[1]CHEN Baoguo,DENG Ming.An incremental algorithm for the neighborhood multi-granulation rough set model based on object update[J].CAAI Transactions on Intelligent Systems,2023,18(3):562-576.[doi:10.11992/tis.202112042]
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An incremental algorithm for the neighborhood multi-granulation rough set model based on object update

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 562-576 Column: 学术论文—知识工程 Public date: 2023-07-05
Title:
An incremental algorithm for the neighborhood multi-granulation rough set model based on object update
Author(s):
CHEN Baoguo;  DENG Ming
School of Computer Science, Huainan Normal University, Huainan 232038, China
Keywords:
data update; rough set; multi-granulation; neighborhood; object change; incremental learning; approximation relation matrix; incremental algorithm
CLC:
TP181
DOI:
10.11992/tis.202112042
Abstract:
The neighborhood multi-granulation rough set model is an important branch of rough set theory. However, in the big data environment, the data is constantly being updated dynamically. In view of the dynamic changes of numerical information system objects, an incremental updating algorithm for neighborhood multi-granulation rough set model is proposed in this paper. Firstly, two kinds of approximation relations between neighborhood class and target approximation set in the neighborhood multi-granulation rough sets are expressed by the matrix method, which are called subset approximation relation matrix and intersection approximation relation matrix, respectively. The neighborhood multi-granulation rough set model is reconstructed by these two approximation relation matrices. Then, the incremental updating of these two approximation relation matrices is studied in the case of increasing and decreasing objects in numerical information system. The theoretical analysis proves that this updating method is of high-efficiency. Finally, the incremental updating algorithm of neighborhood multi-granulation rough set model is designed based on the incremental updating of approximation relation matrices. Experimental results verify the effectiveness and superiority of the proposed incremental algorithm.
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