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Artificial Intelligence Review

Erschienen in:

23.02.2021

On selection of optimal cuts in complete multi-scale decision tables

verfasst von: Yanhong She, Zhuojun Zhao, Mengting Hu, Wenli Zheng, Xiaoli He

Erschienen in: Artificial Intelligence Review | Ausgabe 8/2021

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Abstract

In this paper, a novel optimal scale selection method in complete multi-scale decision tables has been proposed. Unlike the existing approaches in the literature, we employ the tools of granularity trees and cuts for each attribute. Each granularity tree has many different local cuts, which represent various scale selection methods under a specific attribute. Different local cuts collectively forms a global cut of a multi-scale information table, which in turn induces an information table with a mixed scale. One distinct feature of such tables is that the attribute values of different objects may be obtained at different scales for the same attribute. By keeping maximal consistency of the derived mixed-scale decision table, we introduce the notions of optimal cuts in multi-scale decision tables. Then, a comparative study between different types of optimal scale selection methods is performed. Finally, an algorithm is designed to verify the validity of the proposed approach.

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Titel: On selection of optimal cuts in complete multi-scale decision tables
verfasst von: Yanhong She
Zhuojun Zhao
Mengting Hu
Wenli Zheng
Xiaoli He
Publikationsdatum: 23.02.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 8/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-09965-3

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