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Teng, S.H., Lu, M., Yang, A.F., et al. (2016) Efficient Attribute Reduction from the Viewpoint of Discernibility. Information Sciences, 326, 297-314.
https://doi.org/10.1016/j.ins.2015.07.052

has been cited by the following article:

• TITLE: Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

  AUTHORS: Ju Wang, Xinghu Ai, Li Fu

  KEYWORDS: Fuzzy Set, Two Universes, Multi-Granularity Rough Set, Multi-Granularity Neighborhood Fuzzy Rough Set

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.16 No.2, May 10, 2024

  ABSTRACT: The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.