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Variable precision multi-granulation covering rough intuitionistic fuzzy sets

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Abstract

Since the classical Pawlak rough sets model only divides the domain by a single equivalence relation when dealing with uncertain information and ignores the influence of noisy data. In response to this problem, in this paper, we combine covering theory and intuitionistic fuzzy sets theory to propose variable precision multi-granulation covering rough intuitionistic fuzzy set (VPMCIFS) models. First, we construct eight different types of VPMCIFS models based on the minimum description and maximum description by setting two constraints (α, β) in variable precision and introducing them into the multi-granulation covering rough intuitionistic fuzzy set (MCIFS) models. Moreover, we discuss the properties of these models, respectively, and analyze the relationship between the proposed models and existing models. Second, based on these models, we define the approximate accuracy of the VPMCIFS models based on the minimum description and maximum description, respectively. Finally, the validity of these models was verified by the case study of dermatosis patients, and these models were analyzed and compared. By changing the values of α and β, the lower and upper approximations and approximate accuracy of each model are obtained, and the effects of different constraints on the models are analyzed and discussed. The model analyzes the problem from multiple angles and has a certain fault tolerance ability, which can effectively deal with the impact of error and noise data.

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Data sharing is not applicable to this paper as no datasets were generated or analyzed during the current study.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 62076089 and 61772176, the Scientific and Technological Project of Henan Province of China under Grant Nos. 182102210078 and 212102210136.

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Authors and Affiliations

  1. School of Computer and Information Engineering, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Zhan-ao Xue, Meng-meng Jing, Yong-xiang Li & Yu Zheng

  2. Engineering Lab of Intelligence Business & Internet of Things, Henan Province, Xinxiang, China

    Zhan-ao Xue, Meng-meng Jing, Yong-xiang Li & Yu Zheng

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  1. Zhan-ao Xue
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  2. Meng-meng Jing
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  3. Yong-xiang Li
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  4. Yu Zheng
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Contributions

MJ: data curation, investigation, resources, writing—original draft. ZX: conceptualization, funding acquisition, methodology, writing—review & editing. YL and YZ: project administration, supervision.

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Correspondence to Zhan-ao Xue.

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Xue, Za., Jing, Mm., Li, Yx. et al. Variable precision multi-granulation covering rough intuitionistic fuzzy sets. Granul. Comput. 8, 577–596 (2023). https://doi.org/10.1007/s41066-022-00342-1

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