邻域集值双量化粗糙集

李文涛, 李璋, 朱春龙, 徐伟华

doi:10.13371/j.cnki.chin.q.j.m.2021.02.002

数学季刊 >

2021 , Vol. 36 >Issue 2: 122 - 140

DOI: https://doi.org/10.13371/j.cnki.chin.q.j.m.2021.02.002

邻域集值双量化粗糙集

展开

College of Artificial Intelligence, Southwest University,

LI Wen-tao (1987-), male, native of Xianning, Hubei, lecturer of Southwest University, engages in applied mathematics; LI Zhang (1999-), female, native of Jiaozuo, Henan, undergraduate student of Southwest University, engages in artificial intelligence; ZHU Chun-long (2000-), male, native of Ji’an, Jilin, undergraduate student of Southwest University, engages in artificial intelligence; XU Wei-hua (1979-), male, native of Hunyuan, Shanxi, professor of Southwest University, engages in applied mathematics.

收稿日期: 2021-05-05

网络出版日期: 2021-06-24

基金资助

Supported by the College Students Innovation and Entrepreneurship Training Program
project (Grant No. 101202010635586); National Natural Science Foundation of China (Grant No. 61772002,
61976245); Fundamental Research Funds for the Central Universities (Grant No. SWU119063); Scientific and
Technological Project of Construction of Double City Economic Circle in Chengdu-Chongqing Area (Grant No.
KJCX2020009); Science and Technology Research Program of Chongqing Education Commission (Grant No.
KJQN202003806).

收起

Neighborhood-Based Set-Valued Double-Quantitative Rough Sets

Expand

College of Artificial Intelligence, Southwest University,

Received date: 2021-05-05

Online published: 2021-06-24

Supported by

Fold

摘要

Double-quantitative rough approximation, containing two types of quan-
titative information, indicated stronger generalization ability and more accurate data
processing capacity than the single-quantitative rough approximation. In this paper,
the neighborhood-based double-quantitative rough set models are firstly presented in a
set-valued information system. Secondly, the attribute reduction method based on the
lower approximation invariant is addressed, and the relevant algorithm for the approx-
imation attribute reduction is provided in the set-valued information system. Finally,
to illustrate the superiority and the effectiveness of the proposed reduction approach,
experimental evaluation is performed using three datasets coming from the University of
California-Irvine (UCI) repository.

关键词： Attribute reduction; Double quantification; Fuzzy similarity relation; Set-
valued information system

本文引用格式

李文涛, 李璋, 朱春龙, 徐伟华 . 邻域集值双量化粗糙集[J]. 数学季刊, 2021 , 36(2) : 122 -140 . DOI: 10.13371/j.cnki.chin.q.j.m.2021.02.002

Abstract

Key words： Attribute reduction; Double quantification; Fuzzy similarity relation; Set-
valued information system

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract