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Incremental approaches for optimal scale selection in dynamic multi-scale set-valued decision tables

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Abstract

Optimal scale selection is crucial for knowledge discovery in multi-scale decision tables (MDTs). Set-valued decision tables are the generalized versions of single-valued decision information systems and can also be the multi-scale property. Existing researches do not consider the optimal scale selection in a multi-scale set-valued decision table. To address this issue, we introduce the concept of multi-scale set-valued decision tables and study the optimal scale selection problem of multi-scale set-valued decision tables (MSDTs) when the objects are dynamically increased. Firstly, we propose an MSDT model under dominance relations and investigate its characteristics. Secondly, a sequential three-way decision model is established in MSDT. Through reasoning and analyzing the changing trends of the three-way decision at different scales, the optimal scale selection method based on the undetermined degree is proposed. Thirdly, with the increments of the objects in MSDT, we develop incremental algorithms to accelerate optimal scale selection. Finally, a series of comparative experiments on UCI datasets show that our incremental algorithms outperform the non-incremental algorithms in terms of computational complexity.

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Data availability

The experimental data that support the findings of this study are available from UCI Machine Learning Repository, http://archive.ics.uci.edu/ml.

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Acknowledgements

We would like to express our thanks to editors and reviewers for their valuable comments and constructive recommendation. This work was supported by the National Natural Science Foundation of China (Nos. 62266032, Nos. 61976158 and Nos. 61763031), Jiangxi Provincial Natural Science Foundation (Grant No. 20202BAB202018), Training Program for Academic and Technical Leaders in Major Disciplines of Jiangxi Province - Leading Talents Project (20225BCJ22016) and China Postdoctoral Science Foundation (2022M713491).

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Author notes

  1. Yuandong Huang and Yuanjian Zhang have contributed equally to this work.

Authors and Affiliations

  1. College of Mathematics and Computer, Nanchang University, Nanchang, 330031, Jiangxi, People’s Republic of China

    Yuandong Huang & Jianfeng Xu

  2. School of Software, Nanchang University, Nanchang, 330047, Jiangxi, People’s Republic of China

    Jianfeng Xu

  3. China UnionPay Co. Ltd, Shanghai, 201201, People’s Republic of China

    Yuanjian Zhang

  4. Tellhow Software Co., Ltd, Nanchang, 330096, Jiangxi, People’s Republic of China

    Yuandong Huang & Jianfeng Xu

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  1. Yuandong Huang
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Huang conceived the idea for the study. Huang and Zhang performed the research, collected the data, did the comparative experiment analysis and wrote the paper. Corresponding author Xu provided directive comments and research topic.

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Correspondence to Jianfeng Xu.

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Huang, Y., Zhang, Y. & Xu, J. Incremental approaches for optimal scale selection in dynamic multi-scale set-valued decision tables. Int. J. Mach. Learn. & Cyber. 14, 2251–2270 (2023). https://doi.org/10.1007/s13042-022-01761-x

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