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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2021

06.02.2021 | Original Article

Uncertainty measurement for a fuzzy set-valued information system

verfasst von: Zhaowen Li, Zhihong Wang, Qingguo Li, Pei Wang, Ching-Feng Wen

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2021

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Abstract

Uncertainty measurement (UM) can offer new visual angle for data analysis. A fuzzy set-valued information system (FSVIS) which means an information system (IS) where its information values are fuzzy sets. This article investigates UM for a FSVIS. First, a FSVIS is introduced. Then, the distance between two information values of each attribute in a FSVIS is founded. After that, the tolerance relation induced by a given subsystem is acquired by this distance. Moreover, the information structure of this subsystem is brought forward. Additionally, measures of uncertainty for a FSVIS are explored. Eventually, to verify the validity of these measures, statistical effectiveness analysis is carried out. The obtained results will help us understand the intrinsic properties of uncertainty in a FSVIS.
Vorheriger Artikel Sample-based online learning for bi-regular hinge loss
Nächster Artikel Fault detection of railway freight cars mechanical components based on multi-feature fusion convolutional neural network

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Metadaten
Titel
Uncertainty measurement for a fuzzy set-valued information system
verfasst von
Zhaowen Li
Zhihong Wang
Qingguo Li
Pei Wang
Ching-Feng Wen
Publikationsdatum
06.02.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01273-6

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