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Vaguely quantified fuzzy dominance rough set and its incremental maintenance approximation approaches

  • Mathematical methods in data science
  • Published:
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Abstract

Dominance-based rough set approach (DRSA) is an effective knowledge acquisition tool for ordered data, and to meet the needs of practical applications, the DRSA-based extended models have been widely studied. Nevertheless, the collected data in practical applications are always polluted by noise and most of these extended models are very susceptible to noise. In addition, with the arrival of new attributes and the removal of obsolete attributes, the ordered data have the characteristic of attributes evolving over time (i.e., dynamic ordered data). How to efficiently mining knowledge from dynamic ordered data is a meaningful topic. Motivated by these two issues, we first propose a robust fuzzy dominance rough set model to combat noise interference and then develop incremental update approximation mechanisms based on the proposed model for dynamic ordered data. Firstly, the vaguely quantified fuzzy dominance rough set (VQFDRS) and its matrix representation are introduced. Subsequently, we develop the incremental mechanisms to update the approximations of VQFDRS when multiple attributes are added to or deleted from an ordered data, respectively. Then, on this basis, a static calculation approximations algorithm and two incremental calculation approximations algorithms are designed, respectively. Finally, a series of numerical comparative experiments are performed on six public datasets. Experimental results indicate that the VQFDRS has good robustness, and the incremental algorithms are highly efficient compared with static algorithm under the dynamic data environment.

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Enquiries about data availability should be directed to the authors.

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Funding

This work is supported by the National Natural Science Foundation of China (No. 61976130 provided by Keyun Qin, No. 61976245 provided by Weihua Xu).

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Authors and Affiliations

  1. School of Mathematics, Southwest Jiaotong University, Chengdu, 611756, People’s Republic of China

    Lei Yang, Keyun Qin & Han Yang

  2. College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, People’s Republic of China

    Binbin Sang

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

    Weihua Xu

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  1. Lei Yang
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  2. Keyun Qin
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  4. Weihua Xu
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  5. Han Yang
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Contributions

LY helped in conceptualization, writing—original draft, read, and contributed to the manuscript. KQ contributed to supervision; project administration; and funding acquisition. BS was involved in conceptualization, read, and contributed to the manuscript. WX helped in resources; funding acquisition. HY read and contributed to the manuscript.

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Correspondence to Lei Yang.

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Yang, L., Qin, K., Sang, B. et al. Vaguely quantified fuzzy dominance rough set and its incremental maintenance approximation approaches. Soft Comput 27, 867–886 (2023). https://doi.org/10.1007/s00500-022-07598-4

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