research-article

Update of optimal scale in dynamic multi-scale decision information systems

Authors:

Ye FengAuthors Info & Claims

Volume 152, Issue C

Pages 310 - 324

https://doi.org/10.1016/j.ijar.2022.10.020

Published: 01 January 2023 Publication History

Abstract

The problem of optimal scale selection for multi-scale decision information systems is an important issue in the field of granular computing research, especially when data are dynamically updated. Determining the changes of the optimal scale for dynamic data is a goal that has drawn increasing attention from scholars in related fields. For the case of object updating, solving this problem requires finding which characteristic conditions should be satisfied by newly added objects when different cases of the optimal scale occur. However, existing studies only include the sufficient and necessary condition of the optimal scale becoming smaller for dynamically updating objects. Therefore, to complete the theory regarding how the optimal scale will be changed when objects are dynamically updated, it is still necessary to explore the sufficient and necessary conditions for the optimal scale being unchanged or becoming larger. In this paper, we use three-way decision theory to study this problem. Concretely speaking, the uncertain region of three-way decision is used to reveal the change of knowledge at different scales. That is, the obtained results, combined with existing work, provide a nice solution to the problem of finding the changing laws of the optimal scale for object updating in multi-scale decision information systems.

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Cited By

Zhang LLin GWei LKou Y(2024)Feature subset selection for multi-scale neighborhood decision information system via mutual informationArtificial Intelligence Review10.1007/s10462-023-10626-w57:1Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1007/s10462-023-10626-w

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Update of optimal scale in dynamic multi-scale decision information systems

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cover image International Journal of Approximate Reasoning

International Journal of Approximate Reasoning Volume 152, Issue C

Jan 2023

470 pages

ISSN:0888-613X

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 January 2023

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Cited By

Zhang LLin GWei LKou Y(2024)Feature subset selection for multi-scale neighborhood decision information system via mutual informationArtificial Intelligence Review10.1007/s10462-023-10626-w57:1Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1007/s10462-023-10626-w

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