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Tri-level attribute reduction based on neighborhood rough sets

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Abstract

Tri-level attribute reduction is an interesting topic that aims to reduce the data dimensionality from different levels and granularity perspectives. However, existing research exhibits limitations, mainly in handling symbolic data, lack of effective reduction algorithms, and scarcity of data experiments and performance evaluations, which would be an obstacle to the further development of tri-level attribute reduction in theory and application. Hence, we systematically investigate tri-level attribute reduction based on neighborhood rough sets (NRSs) for numerical data. We first give the class-specific and object-specific attribute reduction conditions based on NRS, respectively. Furthermore, we explore and analyze relationships of tri-level reducts. From the perspective of forward and backward reduction, we propose algorithms of class-specific attribute reduction based on dependency degree, and object-specific reduction algorithms based on inconsistency degree. Finally, we introduce a novel metric to validate the efficiency of specific class and specific object attribute reductions. The results of data experiments show the feasibility and effectiveness of tri-level attribute reduction based on NRS in data analysis.

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Data Availability

The data for this study are openly available in UCI Machine Learning Repository at https://archive-beta.ics.uci.edu/datasets.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (62206228), the Sichuan Science and Technology Program of China (2021YJ0085), the Natural Science Foundation of Sichuan Province (2022NSFSC0929), and the Humanities and Social Science Fund of Ministry of Education of China (23YJA630114).

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Authors and Affiliations

  1. Department of Computer Science, Sichuan Normal University, Chengdu, 610068, Sichuan, China

    Lianhui Luo & Jilin Yang

  2. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, 610066, Sichuan, China

    Lianhui Luo, Jilin Yang & Xianyong Zhang

  3. School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Junfang Luo

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  1. Lianhui Luo
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  2. Jilin Yang
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Correspondence to Jilin Yang.

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Luo, L., Yang, J., Zhang, X. et al. Tri-level attribute reduction based on neighborhood rough sets. Appl Intell 54, 3786–3807 (2024). https://doi.org/10.1007/s10489-024-05361-w

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