scholarly journals Attribute reduction in interval-valued information systems based on information entropies

2016 ◽  
Vol 17 (9) ◽  
pp. 919-928 ◽  
Author(s):  
Jian-hua Dai ◽  
Hu Hu ◽  
Guo-jie Zheng ◽  
Qing-hua Hu ◽  
Hui-feng Han ◽  
...  
Keyword(s):  
Information Systems ◽  
Attribute Reduction ◽  
Interval Valued

On variable-precision-based rough set approach to incomplete interval-valued fuzzy information systems and its applications

2021 ◽  
Vol 40 (1) ◽  
pp. 463-475
Author(s):  
Juan Li ◽  
Yabin Shao ◽  
Xiaoding Qi
Keyword(s):  
Information System ◽  
Information Systems ◽  
Attribute Reduction ◽  
Misclassification Rate ◽  
Fuzzy Information ◽  
Attribute Weights ◽  
Variable Precision ◽  
Multiple Attribute ◽  
Rough Approximation ◽  
Interval Valued

 With respect to multiple attribute group decision making problems in which the attribute weights and the expert weights take the form of real numbers and the attribute values take the form of interval-valued uncertain linguistic variable. In this paper, we introduce the idea of variable precision into the incomplete interval-valued fuzzy information system and propose the theory of variable precision rough sets over incomplete interval-valued fuzzy information systems. Then, we give the properties of rough approximation operators and study the knowledge discovery and attribute reduction in the incomplete interval-valued fuzzy information system under the condition that a certain degree of misclassification rate is allowed to exist. Furthermore, a decision rule and decision model are given. Finally, an illustrative example is given and compared with the existing methods, the practicability and effectiveness of this method are further verified.

scholarly journals Multiple Granulation Rough Set Approach to Interval-Valued Intuitionistic Fuzzy Ordered Information Systems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 949
Author(s):  
Zhen Li ◽  
Xiaoyan Zhang
Keyword(s):  
Information Theory ◽  
Pattern Recognition ◽  
Information System ◽  
Information Systems ◽  
Rough Set ◽  
Fuzzy Set ◽  
Equivalence Relation ◽  
Target Concept ◽  
Actual Case ◽  
Interval Valued

As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which do not apply to the IIFS. In this paper, we combine the IIFS with the ordered information system to obtain the interval-valued intuitive fuzzy ordered information system (IIFOIS). On this basis, three types of multiple granulation rough set models based on the dominance relation are established to effectively overcome the limitation mentioned above, which belongs to the interdisciplinary subject of information theory in mathematics and pattern recognition. First, for an IIFOIS, we put forward a multiple granulation rough set (MGRS) model from two completely symmetry positions, which are optimistic and pessimistic, respectively. Furthermore, we discuss the approximation representation and a few essential characteristics for the target concept, besides several significant rough measures about two kinds of MGRS symmetry models are discussed. Furthermore, a more general MGRS model named the generalized MGRS (GMGRS) model is proposed in an IIFOIS, and some important properties and rough measures are also investigated. Finally, the relationships and differences between the single granulation rough set and the three types of MGRS are discussed carefully by comparing the rough measures between them in an IIFOIS. In order to better utilize the theory to realistic problems, an actual case shows the methods of MGRS models in an IIFOIS is given in this paper.

α-Dominance relation and rough sets in interval-valued information systems

2015 ◽  
Vol 294 ◽  
pp. 334-347 ◽  
Author(s):  
Xibei Yang ◽  
Yong Qi ◽  
Dong-Jun Yu ◽  
Hualong Yu ◽  
Jingyu Yang
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Dominance Relation ◽  
Interval Valued

Rough set theory for the interval-valued fuzzy information systems

2008 ◽  
Vol 178 (8) ◽  
pp. 1968-1985 ◽  
Author(s):  
Zengtai Gong ◽  
Bingzhen Sun ◽  
Degang Chen
Keyword(s):  
Information Systems ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fuzzy Information ◽  
Interval Valued
Sign in / Sign up

Export Citation Format

Share Document