Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (8): 83-96.doi: 10.11896/jsjkx.230600185

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-granularity Intuitive Fuzzy Rough Set Model Based on θ Operator

ZHENG Yu, XUE Zhan’ao, LYU Mingming, XU Jiucheng   

  1. College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453000,China
    Engineering Lab of Intelligence Business & Internet of Things,Xinxiang,Henan 453000,China
  • Received:2023-06-23 Revised:2023-11-19 Online:2024-08-15 Published:2024-08-13
  • About author:ZHENG Yu,born in 1997,master.His main research interests include multi-granularity rough set,fuzzy set,intuitional fuzzy set and so on.
    XUE Zhan’ao,born in 1963,Ph.D,professor.His main research interests includeartificial intelligence,fuzzy set,rough set and so on.
  • Supported by:
    National Science Foundation of China(61976082,62076089,62002103),Key Science and Technology Program of Henan Province,China(182102210078,232102210077) and Key Research Projects of Higher Education Institutions in Henan Province,China(24A520019).

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Abstract: In order to solve the problem that it is difficult for decision makers to make accurate judgment when multiple attributes conflict with each other in the multi-attribute decision making.In the intuitive fuzzy approximation space,this paper firstly uses the membership degree,non-membership degree and fuzzy implication operator of intuitive fuzzy set,and proposes the concepts of membership degree and non-membership degree based on θ operator and θ* operator,and proves a series of properties of them.Then,in the intuitive fuzzy set and the multi-granularity rough set,the pessimistic and optimistic models of theintuitive fuzzy rough set based on θ operator are defined,and the related properties of the two models are discussed.Finally,a multi-attribute decision algorithm based on the multi-granularity intuitive fuzzy rough set model based on θ operator is presented.The evaluation of talents introduced by universities and the evaluation of businesses in the green economy supply chain of enterprises are analyzed as examples.The correctness of the proposed method is proved by comparing the results of the optimistic and pessimistic models with those of the existing methods.The effectiveness of the model algorithm is also verified.

Key words: Rough set, Intuitive fuzzy set, Implication operator, Multi-granularity, Multi-attribute decision-making

CLC Number: 

  • TP181
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