Uncertainty measurement for a fuzzy set-valued information system

被引：5
|
作者：
Li, Zhaowen [1 ]
Wang, Zhihong [2 ]
Li, Qingguo [3 ]
Wang, Pei [1 ]
Wen, Ching-Feng [4 ]
机构：
[1] Yulin Normal Univ, Dept Guangxi Educ, Key Lab Complex Syst Optimizat & Big Data Proc, Yulin 537000, Guangxi, Peoples R China
[2] Guangxi Univ Nationalities, Sch Math & Phys, Nanning 530006, Guangxi, Peoples R China
[3] Hunan Univ, Sch Math & Econometr, Changsha 410082, Hunan, Peoples R China
[4] Kaohsiung Med Univ, Res Ctr Nonlinear Anal & Optimizat, Ctr Fundamental Sci, Dept Med Res, Kaohsiung 80708, Taiwan
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2021年 / 12卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Fuzzy set; FSVIS; Information structure; Uncertainty; Measurement; Effectiveness; KNOWLEDGE GRANULATION; ROUGH ENTROPY;
D O I：
10.1007/s13042-020-01273-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Uncertainty measurement (UM) can offer new visual angle for data analysis. A fuzzy set-valued information system (FSVIS) which means an information system (IS) where its information values are fuzzy sets. This article investigates UM for a FSVIS. First, a FSVIS is introduced. Then, the distance between two information values of each attribute in a FSVIS is founded. After that, the tolerance relation induced by a given subsystem is acquired by this distance. Moreover, the information structure of this subsystem is brought forward. Additionally, measures of uncertainty for a FSVIS are explored. Eventually, to verify the validity of these measures, statistical effectiveness analysis is carried out. The obtained results will help us understand the intrinsic properties of uncertainty in a FSVIS.
引用
收藏
页码：1769 / 1787
页数：19
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