Attribute Reduction Based on the Fuzzy Rough Set with Lukasiewicz Implication Operator

Abstract Attribute reduction is a common technique and has made breakthroughs in many aspects. One of the major development directions of attribute reduction is the extended fuzzy rough set models, which is embodied in the selection of fuzzy similarity relations and operators, eventually the derived...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-04, Vol.2224 (1), p.12063
Main Authors: Ma, Yuanhong, Zhong, Yu, Zou, Jietao
Format: Article
Language:English
Subjects:
Algorithms
Fuzzy sets
Operators
Physics
Reduction
Rough set models
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Summary:Abstract Attribute reduction is a common technique and has made breakthroughs in many aspects. One of the major development directions of attribute reduction is the extended fuzzy rough set models, which is embodied in the selection of fuzzy similarity relations and operators, eventually the derived membership functions. In view of the relatively simple selection of implication operators in related research, this article discusses the impact of different implication operators in the fuzzy rough sets. Secondly, the rough set model that relies on Lukasiewicz implication operator is further improved, and the proof of the closure of the new operator on the positive field is given. Finally, a new algorithm is given, and experiments are designed to prove the feasibility of the new algorithm based on eight public data sets.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2224/1/012063