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New neighborhood based rough sets [PDF]
, 2015 Neighborhood based rough sets are important generalizations of the classical rough sets of Pawlak, as neighborhood operators generalize equivalence classes.
I Couso +11 more
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Reduction of Neighborhood-Based Generalized Rough Sets [PDF]
Journal of Applied Mathematics, 2011 Rough set theory is a powerful tool for dealing with uncertainty, granularity, and incompleteness of knowledge in information systems. This paper discusses five types of existing neighborhood-based generalized rough sets.
Zhaohao Wang, Lan Shu, Xiuyong Ding
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An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets [PDF]
Entropy, 2019 Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information ...
Lin Sun +3 more
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Kernel Neighborhood Rough Sets Model and Its Application [PDF]
Complexity, 2018 Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation.
Kai Zeng, Siyuan Jing
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Feature selection based on neighborhood rough sets and Gini index [PDF]
PeerJ Computer Science, 2023 Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection.
Yuchao Zhang +8 more
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Entropy, 2021 Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper ...
Jiucheng Xu +3 more
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Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets
International Journal of Computational Intelligence Systems, 2021 Rough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance.
Yingyue Chen, Yumin Chen
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Journal of Mathematics, 2021 Multigranulation rough set theory is an important tool to deal with the problem of multicriteria information system. The notion of fuzzy β-neighborhood has been used to construct some covering-based multigranulation fuzzy rough set (CMFRS) models through
Zaibin Chang, Lingling Mao
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On Covering-Based Rough Intuitionistic Fuzzy Sets
Mathematics, 2022 Intuitionistic Fuzzy Sets (IFSs) and rough sets depending on covering are important theories for dealing with uncertainty and inexact problems. We think the neighborhood of an element is more realistic than any cluster in the processes of classification ...
R. Mareay +3 more
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Extending Whitney's extension theorem: nonlinear function spaces [PDF]
, 2020 We consider a global, nonlinear version of the Whitney extension problem for manifold-valued smooth functions on closed domains $C$, with non-smooth boundary, in possibly non-compact manifolds.
Roberts, David Michael +1 more
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