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Further Study of Multigranulation T-Fuzzy Rough Sets [PDF]
The Scientific World Journal, 2014 The optimistic multigranulation T-fuzzy rough set model was established based on multiple granulations under T-fuzzy approximation space by Xu et al., 2012.
Wentao Li, Xiaoyan Zhang, Wenxin Sun
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Fuzzy Quantifier-Based Fuzzy Rough Sets [PDF]
Annals of computer science and information systems, 2022 In this paper we apply vague quantification to fuzzy rough sets to introduce fuzzy quantifier-based fuzzy rough sets (FQFRS), an intuitive generalization of fuzzy rough sets.
Adnan Theerens, Chris Cornelis
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Fuzzy rough sets based on fuzzy quantification [PDF]
Fuzzy Sets and Systems, 2023 One of the weaknesses of classical (fuzzy) rough sets is their sensitivity to noise, which is particularly undesirable for machine learning applications. One approach to solve this issue is by making use of fuzzy quantifiers, as done by the vaguely quantified fuzzy rough set (VQFRS) model. While this idea is intuitive, the VQFRS model suffers from both
Adnan Theerens, Chris Cornelis
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A hybrid framework of hesitant fuzzy soft sets and rough sets for uncertainty modelling [PDF]
Scientific ReportsThe process of decision making involves uncertainty due to lack of agreement among experts, inaccuracy in measurements and incomplete information.
Jahanvi +3 more
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Cost-sensitive Multigranulation Approximation of Neighborhood Rough Fuzzy Sets [PDF]
Jisuanji kexue, 2023 Multigranulation neighborhood rough sets are a new data processing mode in the theory of neighborhood rough sets,in which the target concept can be characterized by upper/lower approximate boundaries of optimistic and pessimistic,respectively ...
YANG Jie, KUANG Juncheng, WANG Guoyin, LIU Qun
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Label-based Approach for Dynamic Updating Approximations in Incomplete Fuzzy Probabilistic Rough Sets over Two Universes [PDF]
Jisuanji kexue, 2022 When the missing values are obtained in incomplete fuzzy probabilistic rough sets over two universes,the time efficiency of the traditional static algorithm for updating approximations in incomplete fuzzy probabilistic rough sets over two universes is ...
XUE Zhan-ao, HOU Hao-dong, SUN Bing-xin, YAO Shou-qian
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Optimal Granulation Selection Method Based on Multi-granulation Rough Intuitionistic Hesitant Fuzzy Sets [PDF]
Jisuanji kexue, 2021 In order to obtain the optimal granulations after reduction from the intuitionistic hesitant fuzzy decision information system with multiple attributes,this paper deals with the uncertain information in this system from the perspective of multi-gra ...
XUE Zhan-ao, SUN Bing-xin, HOU Hao-dong, JING Meng-meng
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Roughness of fuzzy soft sets and related results [PDF]
International Journal of Computational Intelligence Systems, 2015 This paper investigates roughness of fuzzy soft sets. A pair of fuzzy soft rough approximations is proposed and their properties are given. Based on fuzzy soft rough approximations, the concept of fuzzy soft rough sets is introduced.
Zhaowen Li, Tusheng Xie
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Three-Way Fuzzy Sets and Their Applications (II)
Axioms, 2022 Recently, the notion of a three-way fuzzy set is presented, inspired by the basic ideas of three-way decision and various generalized fuzzy sets, including lattice-valued fuzzy sets, partial fuzzy sets, intuitionistic fuzzy sets, etc.
Jingqian Wang +2 more
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A Deep Convolutional Neural Network With Fuzzy Rough Sets for FER
IEEE Access, 2020 Existing facial emotion recognition methods do not have high accuracy and are not sufficient practical in real-time applications. We introduce type 2 fuzzy rough sets to develop a Type 2 Fuzzy Rough Convolutional Neural Network, as type 2 fuzzy rough ...
Xiangjian Chen +3 more
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