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A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets

  • Mathematical methods in data science
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Abstract

The data we need to deal with are getting bigger and bigger in recent years, and the same happens to multi-granulation rough sets, so updated schemes have been proposed with the variation of attributes or attribute values in multi-granulation rough sets. This paper puts forward a dynamic mechanism to update the approximations in multi-granulation rough sets when increasing or decreasing objects. Firstly, the relationships between the original approximations and updated approximations are explored when adding or deleting objects and the dynamic processes of updating the lower and upper approximations in optimistic and pessimistic approximations are proposed. Secondly, two corresponding dynamic algorithms and their time complexity are given. Finally, the experimental evaluations show the effectiveness of the proposed dynamic updating algorithms compared with the static algorithm.

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Acknowledgements

This work is supported by the Nature Science Foundation of Shanxi Province (No. 201901D111280).

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  1. School of Mathematics and Computer Science, Shanxi Normal University, Linfen, 041000, Shanxi, People’s Republic of China

    Hong Wang & Jingtao Guan

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  1. Hong Wang
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  2. Jingtao Guan
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Correspondence to Hong Wang.

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Wang, H., Guan, J. A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets. Soft Comput 27, 5257–5276 (2023). https://doi.org/10.1007/s00500-023-07886-7

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