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Artificial Intelligence Review

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30.07.2021

Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems

verfasst von: Wentao Li, Weihua Xu, Xiaoyan Zhang, Jia Zhang

Erschienen in: Artificial Intelligence Review | Ausgabe 3/2022

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Abstract

The main task of local rough set model is to avoid the interference of complicated calculation and invalid information in the formation of approximation space. In this paper, we first present a local rough set model based on dominance relation to make the local rough set theory applicable to the ordered information system, then two kinds of local multigranulation rough set models in the ordered information system are constructed by extending the single granulation environment to a multigranulation case. Moreover, the updating processes of dynamic objects based on global (classical) and local multigranulation rough sets in the ordered information system are analyzed and compared carefully. It is addressed about how the rough approximation spaces of global multigranulation rough set and local multigranulation rough set change when the object set increase or decrease in an ordered information system. The relevant algorithms for updating approximations with dynamic objects on global and local multigranulation rough sets are provided in ordered information systems. To illustrate the superiority and the effectiveness of the proposed dynamic updating approaches in the ordered information system, experimental evaluation is performed using six datasets coming from the University of California-Irvine repository.

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Titel: Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems
verfasst von: Wentao Li
Weihua Xu
Xiaoyan Zhang
Jia Zhang
Publikationsdatum: 30.07.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 3/2022
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-10053-9

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