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The uncertainty measures for covering rough set models

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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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Authors and Affiliations

  1. School of Mathematics and Computer Science, Shanxi Normal University, Linfen, Shanxi, China

    Zhaohao Wang, Xiaoping Zhang & Jianping Deng

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  1. Zhaohao Wang
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  2. Xiaoping Zhang
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  3. Jianping Deng
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Correspondence to Zhaohao Wang.

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Wang, Z., Zhang, X. & Deng, J. The uncertainty measures for covering rough set models. Soft Comput 24, 11909–11929 (2020). https://doi.org/10.1007/s00500-020-05098-x

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