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Soft Computing
Erschienen in: Soft Computing 16/2020

27.06.2020 | Foundations

The uncertainty measures for covering rough set models

verfasst von: Zhaohao Wang, Xiaoping Zhang, Jianping Deng

Erschienen in: Soft Computing | Ausgabe 16/2020

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Abstract

Uncertainty measures are important tools for analyzing various data. However, there are relatively few studies on the uncertainty measures for covering rough set models. In this paper, from the viewpoint of the lower and upper approximations, we propose new uncertainty measures, the lower rough entropy and the upper rough entropy, for covering rough set models. Then, we define the concepts of the joint entropy and the conditional entropy in the covering rough set models. Some important properties of these measures are obtained, and their relationships are investigated. Furthermore, we provide a certain characterization of reducible element of a covering by means of the proposed measures, and apply the proposed rough entropy to evaluate the significance of covering granules of a covering. Finally, we apply these rough entropies to measure a dual degree between covering lower and upper approximations. The theoretical analysis and examples show that the proposed uncertainty measures for covering rough set models are reasonable and useful.
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Metadaten
Titel
The uncertainty measures for covering rough set models
verfasst von
Zhaohao Wang
Xiaoping Zhang
Jianping Deng
Publikationsdatum
27.06.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 16/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-05098-x

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