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Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review
WIREs Data Mining Knowl. Discov., 2021Feature selection aims to select a feature subset from an original feature set based on a certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it has become a key method for data preprocessing in many data mining
Wanting Ji +7 more
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An
The philosophy of soft sets is founded on the fundamental idea of parameterization, while Pawlak's rough sets put more emphasis on the importance of granulation. As a multivalued extension of soft sets, the newly emerging concept called $N$-soft sets can
J. Alcantud, F. Feng, R. Yager
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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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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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International Journal of Computer & Information Sciences, 1982
Summary: We investigate in this paper approximate operations on sets, approximate equality of sets, and approximate inclusion of sets. The presented approach may be considered as an alternative to fuzzy set theory and tolerance theory. Some applications are outlined.
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Summary: We investigate in this paper approximate operations on sets, approximate equality of sets, and approximate inclusion of sets. The presented approach may be considered as an alternative to fuzzy set theory and tolerance theory. Some applications are outlined.
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ROUGH SETS, ROUGH RELATIONS AND ROUGH FUNCTIONS
Fundamenta Informaticae, 1996The paper explores the concepts of approximate relations and functions in the framework of the theory of rough sets. The difficulties with the application of the idea of rough relation to general rough function definition are discussed. The definition of rough function for the domain of real numbers is introduced and its properties are investigated in ...
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Feature Selection Based on Weighted Fuzzy Rough Sets
IEEE transactions on fuzzy systemsFuzzy rough set approaches have received widespread attention across the disciplines of feature selection and rule extraction. When calculating the fuzzy degree of membership of a sample within a specific class, traditional fuzzy rough sets give ...
Changzhong Wang +3 more
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Maximal-Discernibility-Pair-Based Approach to Attribute Reduction in Fuzzy Rough Sets
IEEE transactions on fuzzy systems, 2018Attribute reduction is one of the biggest challenges encountered in computational intelligence, data mining, pattern recognition, and machine learning. Effective in feature selection as the rough set theory is, it can only handle symbolic attributes.
Jianhua Dai +4 more
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2005
Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge, tackled for a long time by philosophers, logicians, and mathematicians, has become also a crucial issue for computer scientists, particularly in the area of artificial intelligence.
Zdzislaw Pawlak +2 more
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Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge, tackled for a long time by philosophers, logicians, and mathematicians, has become also a crucial issue for computer scientists, particularly in the area of artificial intelligence.
Zdzislaw Pawlak +2 more
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Generalized textural rough sets: Rough set models over two universes
Information Sciences, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Altay Uğur, Ayşegül, Diker, Murat
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