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Information Sciences, 2019
At present, Internet-based information processing systems are challenged by different kinds of threats, which lead to various types of damages that in turn result in significant loss of information in Wireless Sensor Networks (WSNs). Moreover, the stream
K. Selvakumar +6 more
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At present, Internet-based information processing systems are challenged by different kinds of threats, which lead to various types of damages that in turn result in significant loss of information in Wireless Sensor Networks (WSNs). Moreover, the stream
K. Selvakumar +6 more
semanticscholar +1 more source
2009
Kernel machines and rough sets are two classes of popular learning techniques. Kernel machines enhance traditional linear learning algorithms to deal with nonlinear domains by a nonlinear mapping, while rough sets introduce a human-like manner to deal with uncertainty in learning.
Qinghua Hu +3 more
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Kernel machines and rough sets are two classes of popular learning techniques. Kernel machines enhance traditional linear learning algorithms to deal with nonlinear domains by a nonlinear mapping, while rough sets introduce a human-like manner to deal with uncertainty in learning.
Qinghua Hu +3 more
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A fuzzy rough set approach to hierarchical feature selection based on Hausdorff distance
Applied intelligence (Boston), 2022Zeyuan Qiu, Hong Zhao
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Soft fuzzy rough set-based MR brain image segmentation
Applied Soft Computing, 2017Graphical abstractDisplay Omitted HighlightsProposed a soft fuzzy rough set-based MR brain image segmentation that can handle the uncertainty related to the vagueness (fuzzy), indecernability (rough sets) in a parameterized representation (soft sets ...
Anupama Namburu +2 more
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Fuzzy Sets and Systems, 1985
In this note we compare notions of rough set and fuzzy set, and we show that these two notions are different.
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In this note we compare notions of rough set and fuzzy set, and we show that these two notions are different.
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Fuzzy Reasoning and Rough Sets
1994Fuzzy concepts are represented by fuzzy sets, so a fuzzy knowledge is defined to be a collection of fuzzy sets (concepts) which includes two constants, 1 (true) and 0 (false). Abstractly, a logical derivation is a finite series of logical operations acting on a given knowledge.
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A noise-aware fuzzy rough set approach for feature selection
Knowledge-Based Systems, 2022Xiaoling Yang +3 more
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RETRACTED: Dominance-based fuzzy rough set approach for incomplete interval-valued data
Journal of Intelligent & Fuzzy Systems, 2018. Interval-valued information systems are general models of single-valued information systems. Interval-values appear to a way to describe the uncertainty that affects the observed objects. However, there are relatively few studies on incomplete interval-
Jianhua Dai +3 more
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Fuzzy Interpolation of Fuzzy Rough Sets
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2022Dávid Gégény, Szilveszter Kovács
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