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Attribute Reduction Approach Using Evidence Theory for Hesitant Fuzzy Data Sets

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Abstract

For decision makers in various fields, the best way to deal with complicated decision-making problems quickly and efficiently is to simplify the complicated problems as much as possible. Inspired by this kind of social management demand, this paper proposes a novel attribute reduction method using evidence theory in hesitant fuzzy data sets. The method ensures belief and plausibility sum and considers internal and external significance measures. It extends classical evidence theory to hesitant fuzzy data sets and introduces internal and external belief significance measures and internal and external plausibility significance measures based on belief and plausibility functions. The properties of belief and plausibility reduction in hesitant fuzzy data sets are studied, along with the relationship between internal and external significance measures. The attribute reduction method based on significance measure has significant effects on complex information systems and can improve the execution efficiency of decision-makers. And the impressive application value of these theories is demonstrated.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62376229).

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Authors and Affiliations

  1. Fujian Key Laboratory of Modern Communication and Beidou Positioning Technology in Universities, Quanzhou University of Information Engineering, Quanzhou, 362000, Fujian, People’s Republic of China

    **nhong Meng

  2. College of Artificial Intelligence, Southwest University, Chongqing, 400715, People’s Republic of China

    Weihua Xu

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  1. **nhong Meng
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  2. Weihua Xu
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Correspondence to Weihua Xu.

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Meng, X., Xu, W. Attribute Reduction Approach Using Evidence Theory for Hesitant Fuzzy Data Sets. Int. J. Fuzzy Syst. (2024). https://doi.org/10.1007/s40815-023-01674-z

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