Beam-Influenced Attribute Selector for Producing Stable Reduct

Wangwang Yan, Jing Ba, Taihua Xu, Hualong Yu, Jinlong Shi and Bin Han
Additional contact information
Wangwang Yan: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Jing Ba: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Taihua Xu: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Hualong Yu: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Jinlong Shi: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Bin Han: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Mathematics, 2022, vol. 10, issue 4, 1-20

Abstract: Attribute reduction is a critical topic in the field of rough set theory. Currently, to further enhance the stability of the derived reduct, various attribute selectors are designed based on the framework of ensemble selectors. Nevertheless, it must be pointed out that some limitations are concealed in these selectors: (1) rely heavily on the distribution of samples; (2) rely heavily on the optimal attribute. To generate the reduct with higher stability, a novel beam-influenced selector (BIS) is designed based on the strategies of random partition and beam. The scientific novelty of our selector can be divided into two aspects: (1) randomly partition samples without considering the distribution of samples; (2) beam-based selections of features can save the selector from the dependency of the optimal attribute. Comprehensive experiments using 16 UCI data sets show the following: (1) the stability of the derived reducts may be significantly enhanced by using our selector; (2) the reducts generated based on the proposed selector can provide competent performance in classification tasks.

Keywords: attribute reduction; random partition; rough set; stability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/4/553/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/4/553/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:4:p:553-:d:746746

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().