Results 161 to 170 of about 185,811 (251)
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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.
Henri Prade
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Information Sciences, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei-Zhi Wu +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei-Zhi Wu +2 more
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On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems, 2005Rough sets and fuzzy sets have been proved to be powerful mathematical tools to deal with uncertainty, it soon raises a natural question of whether it is possible to connect rough sets and fuzzy sets. The existing generalizations of fuzzy rough sets are all based on special fuzzy relations (fuzzy similarity relations, T-similarity relations), it is ...
D S Yeung, Degang Chen, Wang Xizhao
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Fuzzy Sets and Systems, 1992
The authors introduce the concept of a fuzzy rough set by fuzzifying rough sets. They show some easy properties of fuzzy rough sets.
Nanda, S., Majumdar, S.
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The authors introduce the concept of a fuzzy rough set by fuzzifying rough sets. They show some easy properties of fuzzy rough sets.
Nanda, S., Majumdar, S.
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On Robust Fuzzy Rough Set Models
IEEE Transactions on Fuzzy Systems, 2012 Rough sets, especially fuzzy rough sets, are supposedly a powerful mathematical tool to deal with uncertainty in data analysis. This theory has been applied to feature selection, dimensionality reduction, and rule learning.
Qinghua Hu, Shuang An, David Zhang
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Feature Selection With Local Density-Based Fuzzy Rough Set Model for Noisy Data
IEEE transactions on fuzzy systems, 2023Fuzzy rough set theory can model uncertainty in data and has been applied to feature selection for machine learning tasks. The existence of noise in data is one of the reasons for data uncertainty. However, most classical fuzzy rough set models are often
Xiaoling Yang +6 more
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A novel fuzzy rough set model with fuzzy neighborhood operators
Information Sciences, 2021It is not widely acknowledged that none of existing fuzzy β -neighborhood operators satisfies the reflexivity when β ≠ 1 . To overcome this shortcoming, four types of fuzzy β -neighborhood operators are redefined, which shows that two redefined operators
Jin Ye, J. Zhan, Weiping Ding, H. Fujita
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Applied Soft Computing, 2021
Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has been successfully applied to the fields of attribute reduction, rule extraction, classification tree induction, etc.
Zhong Yuan +5 more
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Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has been successfully applied to the fields of attribute reduction, rule extraction, classification tree induction, etc.
Zhong Yuan +5 more
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Textures and fuzzy unit operations in rough set theory: An approach to fuzzy rough set models
Fuzzy Sets Syst., 2017 In this paper, an approach for the fuzzy rough set models is presented using textures and a fuzzy version of the unit operations of Wybraniec-Skardowska. First, a fuzzy unit operation and fuzzy unit co-operation on fuzzy lattices are defined.
Murat Diker
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Information Sciences, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohua Banerjee, Sankar K. Pal
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Mohua Banerjee, Sankar K. Pal
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