Variable-precision-dominance-based rough set approach to interval-valued information systems

Zhang, Hongying; Leung, Yee; Zhou, Lihua

doi:10.1016/j.ins.2013.04.031

Cited by 74 publications

(20 citation statements)

References 36 publications

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“…also proposed the dominance‐based rough fuzzy model. To deal with the inconsistencies caused by errors in recording, measurement, and observation, Inuiguchi et al . proposed a variable‐precision dominance relation based on rough set approach and studied the attribute reduction.…”

Section: Introductionmentioning

confidence: 99%

Tolerance Dominance Relation in Incomplete Ordered Decision Systems

Guan

Huang

Han

2017

Int. J. Intell. Syst.

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To deal with inconsistencies coming from consideration of criteria, that is, attributes with preference-ordered domains (scales), such as product quality, market share, and debt ratio, the dominance-based rough set approach has been proposed. However, it is not much useful for analyzing incomplete information. In this paper, we attempt to present research focusing on incomplete ordered decision systems where all attributes are considered as criteria and some objects' criterion values are unknown. Under the assumption that all unknown values are the "do not care" semantics, the concept of tolerance dominance relation is introduced. Then, the rough approximations induced by it are obtained. And their properties are investigated. Especially, the important properties of rough approximations, such as rough inclusion, complementarity, identity, and monotonicity, are preserved well-known in the presented methodology. Moreover, an example is employed to illustrate these results. We expand the potential applications of dominance-based rough set approach. C 2017 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

Tolerance Dominance Relation in Incomplete Ordered Decision Systems

Guan

Huang

Han

2017

Int. J. Intell. Syst.

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“…Inclusion measure is a hot topic in the study of fuzzy set theory and its applications to many fields, such as rough set theory and group decision making [18,30,44,49,[55][56][57][58]. In the theoretic study, there are seven axiomatization approaches have been given for fuzzy inclusion measures.…”

Section: Introductionmentioning

confidence: 99%

On inclusion measures of intuitionistic and interval-valued intuitionistic fuzzy values and their applications to group decision making

Zhang

Yang

Yue

2015

Int. J. Mach. Learn. & Cyber.

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Ranking intuitionistic fuzzy values (IFVs) and interval-valued intuitionistic fuzzy values (IVIFVs) is an important and necessary work in intuitionistic fuzzy group decision making. Since the set of all IFVs is a poset and inclusion measure indicates the degree to which a given element of a poset is contained in another one. This paper studies hybrid monotonic (HM) inclusion measures of IFVs and IVIFVs respectively and discuss their applications to group decision making. Firstly, HM inclusion measure is defined on the posets of all IFVs and IVIFVs respectively. Then HM inclusion measures are studied by constructive approach. Furthermore, the HM inclusion measures are employed to make intuitionistic and interval-valued intuitionistic fuzzy group decisions. Lastly, practical examples are provided to illustrate the developed approaches respectively.

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“…(DTRS) [2,7,8,[10][11][12][13][14][15][16][17][18][19][20][21][22]26,27,[32][33][34][35][36]38] extended RST by considering probabilistic information of objects into a set of positive, negative or boundary regions, by introducing a pair of thresholds (a, b). When proper thresholds are used, we can derive several existing probabilistic rough set models, such as the variable precision rough set (VPRS) [40,41], the game-theoretic rough set (GTRS) [2,12], and the Bayesian rough set (BRS) [23] from the decision-theoretic rough set model.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we use the original meaning of precision parameter b [1,40], which is equivalent to the b parameter of the threshold pair (a, b) in DTRS. Therefore, we also provide a symmetric threshold (1 À b, b) method from the viewpoint of DTRS [2,7,8,[10][11][12][13][14][15][16][17][18][19][20][21][22]26,27,[32][33][34][35][36]38].…”

Section: Introductionmentioning

confidence: 99%

“…Despite that all these methods aim at searching for an optimal precision parameter objectively, they still focus on a special b value and attribute reduction anomalies will occur inevitably under the classic reduct definition [39]. Three-region decision about DTRS [2,7,8,[10][11][12][13][14][15][16][17][18][19][20][21][22]26,27,[32][33][34][35][36]38] is currently a hot topic. Yao [10,[34][35][36], Jia [15,16] and others [2,12,19,20] have provided several methods for finding the parameters.…”

Section: Introductionmentioning

confidence: 99%

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