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Information Sciences, 2020
Conflict analysis aims to identify the intrinsic reasons and find a feasible consensus strategy for a conflict situation. Rough set theory was used to study conflict analysis decision-making in the late 90s.
B. Sun +3 more
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Conflict analysis aims to identify the intrinsic reasons and find a feasible consensus strategy for a conflict situation. Rough set theory was used to study conflict analysis decision-making in the late 90s.
B. Sun +3 more
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Active Incremental Feature Selection Using a Fuzzy-Rough-Set-Based Information Entropy
IEEE transactions on fuzzy systems, 2020Feature selection is a popular technique of preprocessing data. In order to deal with dynamic or large data, incremental feature selection has been developed, in which the features selected from existing data are integrated with those mined from both ...
Xiao Zhang +4 more
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ROUGH FUZZY SETS AND FUZZY ROUGH SETS*
International Journal of General Systems, 1990The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy set, sometimes with a view to prove that one is more general, or, more useful than the other. In this paper we argue that both notions aim to different purposes.
Dubois, Didier, Prade, Henri
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Novel fuzzy rough set models and corresponding applications to multi-criteria decision-making
Fuzzy Sets Syst., 2020By means of a fuzzy coimplication operator J and a triangular conorm S , we set forth two pairs of ( J , S ) -fuzzy rough set models, which are generalizations of fuzzy rough sets.
Kai Zhang, J. Zhan, Weizhi Wu
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Communications of the ACM, 1995
Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge ...
Pawlak, Zdzisław +3 more
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Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge ...
Pawlak, Zdzisław +3 more
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2015
As quantitative generalizations of Pawlak rough sets, probabilistic rough sets consider degrees of overlap between equivalence classes and the set. An equivalence class is put into the lower approximation if the conditional probability of the set, given the equivalence class, is equal to or above one threshold; an equivalence class is put into the ...
Yao, Yiyu +2 more
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As quantitative generalizations of Pawlak rough sets, probabilistic rough sets consider degrees of overlap between equivalence classes and the set. An equivalence class is put into the lower approximation if the conditional probability of the set, given the equivalence class, is equal to or above one threshold; an equivalence class is put into the ...
Yao, Yiyu +2 more
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A rough set-based bio-inspired fault diagnosis method for electrical substations
, 2020Imprecision and uncertainty in the alarm messages may significantly affect the accuracy and reliability of substation fault diagnosis results. To deal with that, a new rough set-based bio-inspired fault diagnosis method (RSBFDM) is proposed in this paper.
Tao Wang +4 more
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2015
This chapter reviews three formulations of rough set theory, i. e., element-based definition, granule-based definition, and subsystem-based definition. These formulations are adopted to generalize rough sets from three directions. The first direction is to use an arbitrary binary relation to generalize the equivalence relation in the element-based ...
Yao, J, CIUCCI, DAVIDE ELIO, Zhang, Y.
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This chapter reviews three formulations of rough set theory, i. e., element-based definition, granule-based definition, and subsystem-based definition. These formulations are adopted to generalize rough sets from three directions. The first direction is to use an arbitrary binary relation to generalize the equivalence relation in the element-based ...
Yao, J, CIUCCI, DAVIDE ELIO, Zhang, Y.
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Information Sciences, 2019
By means of a fuzzy logical implicator and a t-norm (respectively denoted I and T ), we introduce covering based multigranulation ( I , T ) -fuzzy rough set models from fuzzy β-neighborhoods.
J. Zhan, B. Sun, J. Alcantud
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By means of a fuzzy logical implicator and a t-norm (respectively denoted I and T ), we introduce covering based multigranulation ( I , T ) -fuzzy rough set models from fuzzy β-neighborhoods.
J. Zhan, B. Sun, J. Alcantud
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A neighborhood rough set model with nominal metric embedding
Information Sciences, 2020Rough set theory is an essential tool for measuring uncertainty, which has been widely applied in attribute reduction algorithms. Most of the related researches focus on how to update the lower and the upper approximation operator to match data ...
Sheng Luo +4 more
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