Fuzzy set-valued information systems and their homomorphisms based on data compression-Reference-Cited by-同舟云学术

Fuzzy set-valued information systems and their homomorphisms based on data compression

Published:2022-07-21 Issue:3 Volume:43 Page:3311-3321
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Wang Pei¹,Zhao Zhengwei²,Wang Zhihong³,Li Zhaowen¹

Affiliation:

1. Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, Guangxi, P.R. China

2. School of Mathematics and Physics, Guangxi Minzu University, Nanning, Guangxi, P.R. China

3. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan, P.R. China

Abstract

A fuzzy set-valued information system (FSVIS) is a special information system (IS) where the value of an object under each attribute or each attribute value is a fuzzy set. Homomorphism is a powerful mathematical tool to deal with FSVISs, which can be used to study relationships among them. Based on data compression, we obtain some characterizations about FSVISs and their homomorphisms. First, some homomorphisms between FSVISs are introduced. After that, attribute reduction based on tolerance relation in a FSVIS is studied. Eventually, we get invariant characterizations of FSVISs based on some special homomorphisms under data compression.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference33 articles.

1. Non-iterative procedure incorporated into the fuzzy identification on a hybrid method of functional randomization for time series forecasting models;Carvalho;Applied Soft Computing,2019

2. Chen S.L. , Li J.G. and Wang X.G. , Fuzzy sets theory and its applications, Chinese Scientific Publishers, Beijing, 2005.

3. Feature selection via normative fuzzy information weight with application into tumor classification;Dai;Applied Soft Computing,2020

4. Maximal-discernibility-pair-based approach to attribute reduction in fuzzy rough sets;Dai;IEEE Transactions on Fuzzy Systems,2018

5. Maximal discernibility pairs based approach to attribute reduction in fuzzy rough sets;Dai;IEEE Transactions on Fuzzy Systems,2018