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A novel granular variable precision fuzzy rough set model and its application in fuzzy decision system

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Abstract

The comparable property (or inclusion property) is the basic property of rough set theory. Unfortunately, the well-known granular variable precision fuzzy rough set does not satisfy the comparable property. To remedy this gap, a novel granular variable precision fuzzy rough set model with comparable property is proposed and discussed. Firstly, by analyzing the existing granular precision fuzzy rough set models, we find the reason that they do not satisfy the comparable property. Then, we give a new granular precision fuzzy rough set model with the comparable property. Secondly, we study the basic properties, characterization theorem and accuracy measure of the new model. Thirdly, we apply the novel model in the study of fuzzy decision system. An attribute reduction method is developed, and the corresponding attribute reduction algorithm is designed. Finally, by using public accessible datasets, we make a series of experiments to verify the effectiveness and reliability of our method and make comparisons with some existing attribute reduction methods.

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Acknowledgements

We are very grateful to the anonymous reviewers for their helpful suggestions.

Funding

This work was supported by National Natural Science Foundation of China (Grant Nos. 12171220, 61976194, 62076221), and Natural Science Foundation of Shandong Province (Grant No. ZR2020MA042), and Discipline with Strong Characteristics of Liaocheng University-Intelligent Science and Technology under Grant 319462208.

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Authors and Affiliations

  1. College of Mathematics and Sciences, Liaocheng University, Liaocheng, China

    Dan-Dan Zou, Yao-Liang Xu & Ling-Qiang Li

  2. School of Information Engineering, Zhejiang Ocean University, Zhoushan, China

    Wei-Zhi Wu

  3. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, China

    Wei-Zhi Wu

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  1. Dan-Dan Zou
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  2. Yao-Liang Xu
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  3. Ling-Qiang Li
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  4. Wei-Zhi Wu
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All authors contributed equally and significantly in writing this paper.

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Correspondence to Ling-Qiang Li.

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Zou, DD., Xu, YL., Li, LQ. et al. A novel granular variable precision fuzzy rough set model and its application in fuzzy decision system. Soft Comput 27, 8897–8918 (2023). https://doi.org/10.1007/s00500-022-07796-0

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