2012
DOI: 10.4304/jsw.7.3.551-563
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Intuitionistic Fuzzy Dominance–based Rough Set Approach: Model and Attribute Reductions

Abstract: The dominance-based rough set approach plays an important role in the development of the rough set theory. It can be used to express the inconsistencies coming from consideration of the preference-ordered domains of the attributes. The purpose of this paper is to further generalize the dominance-based rough set model to fuzzy environment. The constructive approach is used to define the intuitionistic fuzzy dominance-based lower and upper approximations respectively. Basic properties of the intuitionistic fuzzy… Show more

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Cited by 6 publications
(3 citation statements)
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References 75 publications
(61 reference statements)
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“…In other words, it is a superimposed field of various kinds of source fields with different mediums under the ground. The migration imaging method can relocate the up going field to each source field successively, i.e., the interface of different mediums in a reverse time transmission [17][18][19].…”
Section: Migration Imaging Of Mtmentioning
confidence: 99%
“…In other words, it is a superimposed field of various kinds of source fields with different mediums under the ground. The migration imaging method can relocate the up going field to each source field successively, i.e., the interface of different mediums in a reverse time transmission [17][18][19].…”
Section: Migration Imaging Of Mtmentioning
confidence: 99%
“…After that, Takeuti and Titani [18] defined intuitionistic fuzzy (IF) logic and IF set by consideration the propositions valuated into the range [0, 1]. In recent years, the research on the combination of IFS and the rough set has received widespread attention [19–22]. After that, a new dominance relation was introduced by Zhang and Chen [23], and then they proposed generalised dominance‐based IF rough set model.…”
Section: Introductionmentioning
confidence: 99%
“…Rough set theory is a powerful mathematical tool for dealing with vague, imprecise, incomplete and uncertain data, and the theory has been successfully applied in a number of areas such as machine learning, expert system, pattern recognition, decision analysis, and knowledge discovery in databases [1][2][3][4][5][6][7]22,23]. Attribute reduction is one important part researched in rough set theory.…”
Section: Introductionmentioning
confidence: 99%