Constructing Robust Fuzzy Rough Set Models Based on Three-way Decisions

In 1982, Pawlak proposed the concept of rough sets with a practical purpose of representing indiscernibility of elements or objects in the presence of information systems. Even if it is easy to analyze, the rough set theory built on a partition induced by equivalence relation may not provide a realistic view of relationships between elements in real-world applications. Here, coverings of, or nonequivalence relations on, the universe can be considered to represent a more realistic model instead of a partition in which a generalized model of rough sets was proposed. In this paper, first a weak fuzzy similarity relation is introduced as a more realistic relation in representing the relationship between two elements of data in real-world applications. Fuzzy conditional probability relation is considered as a concrete example of the weak fuzzy similarity relation. Coverings of the universe is provided by fuzzy conditional probability relations. Generalized concepts of rough approximations and rough membership functions are proposed and defined based on coverings of the universe. Such generalization is considered as a kind of fuzzy rough set. A more generalized fuzzy rough set approximation of a given fuzzy set is proposed and discussed as an alternative to provide interval-value fuzzy sets. Their properties are examined.

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Unsupervised fuzzy-rough set-based dimensionality reduction

Information Sciences ◽

10.1016/j.ins.2012.12.001 ◽

2013 ◽

Vol 229 ◽

pp. 106-121 ◽

Cited By ~ 44

Author(s):

Neil Mac Parthaláin ◽

Richard Jensen

Keyword(s):

Dimensionality Reduction ◽

Rough Set ◽

Fuzzy Rough Set

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A three-way decision method based on fuzzy rough set models under incomplete environments

Information Sciences ◽

10.1016/j.ins.2021.06.088 ◽

2021 ◽

Author(s):

Jin Ye ◽

Jianming Zhan ◽

Bingzhen Sun

Keyword(s):

Rough Set ◽

Fuzzy Rough Set ◽

Decision Method

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Details Enhancement In Low Contrast Region Of Inspection Image Based On Fuzzy Rough Set

10.1109/iccst53801.2021.00100 ◽

2021 ◽

Author(s):

Junbao Zheng ◽

Junpeng Ji ◽

Xiu Liu

Keyword(s):

Rough Set ◽

Fuzzy Rough Set ◽

Low Contrast

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Covering based multigranulation(I,T)-fuzzy rough set models and applications in multi-attribute group decision-making

Information Sciences ◽

10.1016/j.ins.2018.10.016 ◽

2019 ◽

Vol 476 ◽

pp. 290-318 ◽

Cited By ~ 100

Author(s):

Jianming Zhan ◽

Bingzhen Sun ◽

José Carlos R. Alcantud

Keyword(s):

Decision Making ◽

Rough Set ◽

Group Decision Making ◽

Group Decision ◽

Fuzzy Rough Set

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An approach to evaluation of emergency plans for unconventional emergency events based on soft fuzzy rough set

Kybernetes ◽

10.1108/k-03-2014-0055 ◽

2016 ◽

Vol 45 (3) ◽

pp. 461-473 ◽

Cited By ~ 13

Author(s):

Sun Bingzhen ◽

Ma Weimin

Keyword(s):

Decision Making ◽

Emergency Management ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Management Decision ◽

Fuzzy Rough Set ◽

Emergency Plans ◽

Content Type ◽

Emergency Events

Purpose – The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology. Design/methodology/approach – In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory. Findings – This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events. Originality/value – The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

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