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A Fuzzy Decision-Theoretic Rough Set Approach for Type-2 Fuzzy Conditional Information Systems and Its Application in Decision-Making

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Abstract

The decision-theoretic rough set model based on Bayesian decision theory proposes a framework for studying rough set approximations using probabilistic theory, it has become an important research direction of rough set theory. In order to extend the theory of decision-theoretic rough set, this article is devoted to studying the fuzzy decision-theoretic rough set model in type-2 fuzzy conditional information systems. The type-2 fuzzy similarity relations are induced though computing the measure of similarity between objects for each attribute. Then, we use intersection operation of type-2 fuzzy sets to aggregate the multiple induced fuzzy similarity relations. Next, a fuzzy decision-theoretic rough set approach for type-2 fuzzy conditional information systems is proposed by means of inclusion degree of type-2 fuzzy sets. Finally, an example is considered to illustrate the effectiveness of the proposed method, which is helpful for applying these theories to deal with practical issues.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61976089, 61473259, 61070074, 60703038), the Natural Science Foundation of Hunan Province (2021JJ30451), and the Hunan Provincial Science & Technology Project Foundation (2018RS3065, 2018TP1018).

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321004, China

    Xiaofeng Liu

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan, 410081, China

    Xiaofeng Liu & Jianhua Dai

  3. College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China

    Jianhua Dai

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  1. Xiaofeng Liu
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Correspondence to Jianhua Dai.

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Liu, X., Dai, J. A Fuzzy Decision-Theoretic Rough Set Approach for Type-2 Fuzzy Conditional Information Systems and Its Application in Decision-Making. Int. J. Fuzzy Syst. 24, 622–634 (2022). https://doi.org/10.1007/s40815-021-01167-x

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