Adjustable Fuzzy Rough Reduction: A Nested Strategy | Zendy

Ying Shi | Zendy; Hui Qi | Zendy; Xiaofang Mu | Zendy; Mingxing Hou | Zendy

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Adjustable Fuzzy Rough Reduction: A Nested Strategy

Author(s) -

Ying Shi,

Hui Qi,

Xiaofang Mu,

Mingxing Hou

Publication year - 2021

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2021/5513722

Subject(s) - reduct , rough set , reduction (mathematics) , extension (predicate logic) , parameterized complexity , data mining , heuristic , computer science , fuzzy logic , fuzzy set , set (abstract data type) , mathematics , algorithm , artificial intelligence , geometry , programming language

As a crucial extension of Pawlak's rough set, a fuzzy rough set has been successfully applied in real-valued attribute reduction. Nevertheless, the traditional fuzzy rough set is not provided with adjustable ability due to the maximal and minimal operators. It follows that the associated measure for attribute evaluation is not always appropriate. To alleviate such problems, a novel adjustable fuzzy rough set model is presented and further introduced into the parameterized attribute reduction. Additionally, the inner relationship between the appointed parameter and the reduct result is discovered, and thereby a nested mechanism is adopted to accelerate the searching procedure of reduct. Experiments demonstrate that the proposed heuristic algorithm can offer us more stable reducts with higher computational efficiency as compared with the traditional approaches.

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