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Three-way decisions: beyond rough sets and granular computing

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Abstract

With the fast developments of three-way decisions (3WD), this paper systematically summarizes the development track and evolution process of 3WD in recent decades. Firstly, the historical context, internal connections and relations between 3WD and rough sets are carefully investigated. Then, we discuss the methodology of 3WD via granular computing with “multi-level” strategy and “multi-view” strategy. Two novel 3WD generalized models with multilevel structure and multiview structure, as well as an integration framework of multilevel and multiview, are analyzed and discussed detailedly. Finally, this paper presents the research status and future research topics of 3WD.

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Notes

  1. By Zhilin Bian; Translated by Xianyi Yang and Naidie Dai.

  2. By Bai Li; Translated by Burton Watson.

  3. By Si Su; Translated by Burton Watson.

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Acknowledgements

This work is partially supported by the National Science Foundation of China (Nos. 61876157, 71571148, 61773324), and the Yanghua Scholar Plan (Part A) of SWJTU. The authors thank for Dr. Yiyu Yao for his help and guidance in last ten years, and some materials of this paper are coming from Dr. Yao’s reports in China. The authors also thank for Dr. Jinhai Li for his encouragement and support.

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Authors and Affiliations

  1. School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China

    Dun Liu

  2. School of Economic Information Engineering, Southwest University of Finance and Economics, Chengdu, 611130, People’s Republic of China

    Xin Yang

  3. Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, People’s Republic of China

    Tianrui Li

  4. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, People’s Republic of China

    Tianrui Li

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  1. Dun Liu
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  2. Xin Yang
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Correspondence to Dun Liu.

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Liu, D., Yang, X. & Li, T. Three-way decisions: beyond rough sets and granular computing. Int. J. Mach. Learn. & Cyber. 11, 989–1002 (2020). https://doi.org/10.1007/s13042-020-01095-6

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