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Dynamically updating approximations based on multi-threshold tolerance relation in incomplete interval-valued decision information systems

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Abstract

With the development of society, data noise and other factors will cause the incompleteness of information systems. Objects may increase or decrease over time in information systems. The classical information system can be extended to the incomplete interval-valued decision information system (IIDIS) that is the researching object of this paper. Incremental learning technique is a significant method for solving approximate sets under dynamic data. This article defines a multi-threshold tolerance relation based on the set pair analysis theory and establishes a rough set model in IIDIS. Then, several methods and algorithms for statically/dynamically solving approximate sets are shown. Finally, comparative experiments from six UCI data sets show both dynamic algorithms take less time than the static algorithm to calculate the approximate sets no matter how object set changes.

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Acknowledgements

We would like to express our thanks to the Editor-in-Chief, handling associate editor and anonymous referees for his/her valuable comments and constructive suggestions. This paper is supported by the National Natural Science Foundation of China (Nos. 61472463, 61772002) and the Fundamental Research Funds for the Central Universities (No. XDJK2019B029).

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Authors and Affiliations

  1. School of Science, Chongqing University of Technology, Chongqing, People’s Republic of China

    Bingyan Lin

  2. College of Artificial Intelligence, Southwest University, Chongqing, 400715, People’s Republic of China

    Xiaoyan Zhang & Weihua Xu

  3. School of Computer Science and Technology, Southwest Petroleum University, Chengdu, People’s Republic of China

    Yanxue Wu

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  1. Bingyan Lin
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  2. Xiaoyan Zhang
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  4. Yanxue Wu
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Correspondence to Xiaoyan Zhang.

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Lin, B., Zhang, X., Xu, W. et al. Dynamically updating approximations based on multi-threshold tolerance relation in incomplete interval-valued decision information systems. Knowl Inf Syst 62, 1063–1087 (2020). https://doi.org/10.1007/s10115-019-01377-8

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