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A trilevel analysis of uncertainty measuresin partition-based granular computing

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Abstract

Uncertainty measure is one of the most significant concepts and fundamental issues in granular computing. Nowadays, there have been extensive studies on various uncertainty measures for quantifying diverse properties and associations of granules and granular structures. However, there is a lack of a systematic study for uncertain measures. Based on a trilevel thinking framework, this paper presents a systematic review and analysis of uncertainty measures used in partition-based models of granular computing. At an object level, a granule level and a granular structure level, we categorize uncertainty measures for describing the properties and the associations of objects, granules, and partitions respectively. Moreover, we illustrate how to construct an uncertainty measure at a higher level from a lower level. At last, we discuss several potential directions to design other new uncertainty measures for partition-based granular computing.

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Acknowledgements

This work was partially supported by a Discovery Grant from NSERC, Canada, the National Natural Science Foundation of China (Nos. 61703363, U21A20473), the Applied Basic Research Program of Shanxi Province (No. 201901D211462), the Key R&D Program of Shanxi Province (International Cooperation (No. 201903D421041)), the Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (No. CICIP2018008).

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  1. School of Mathematics and Information Technology, Yuncheng University, Yuncheng, 044000, Shanxi, China

    Baoli Wang

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, Shanxi, China

    Jiye Liang

  3. Department of Computer Science, University of Regina, Regina, S4S 0A2, SK, Canada

    Yiyu Yao

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  1. Baoli Wang
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Wang, B., Liang, J. & Yao, Y. A trilevel analysis of uncertainty measuresin partition-based granular computing. Artif Intell Rev 56, 533–575 (2023). https://doi.org/10.1007/s10462-022-10177-6

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