Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (6): 45-57.doi: 10.6040/j.issn.1672-3961.0.2024.111

• Machine Learning & Data Mining • Previous Articles    

Incremental attribute reduction of interval-valued decision-making information systems from the perspective of knowledge granularity

QIU Liqin1,2, WANG Lei1,2*, YU Yue1,2, SUN Yahui1,2   

  1. QIU Liqin1, 2, WANG Lei1, 2*, YU Yue1, 2, SUN Yahui1, 2(1. School of Information Engineering, Jiangxi University of Water Resources and Electric Power, Nanchang 330099, Jiangxi, China;
    2. Jiangxi Province Key Laboratory of Smart Water Conservancy(Jiangxi University of Water Resources and Electric Power), Nanchang 330099, Jiangxi, China
  • Published:2025-12-22

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Abstract: In view of the inefficiency of non-incremental attribute reduction in interval-valued decision-making information systems, the concept of knowledge granularity was extended to interval-valued decision-making information systems, and incremental attribute reduction in interval-valued decision-making information systems was systematically investigated from the viewpoint of knowledge granularity. The concept of tolerance degree was introduced into interval-valued decision-making information systems and the measurement method of interval-valued tolerance degree was improved; the tolerance relation was determined according to the tolerance degree and the corresponding tolerance relation matrix was constructed. This led to an calculation method of the knowledge granularity in the interval-valued decision-making information system based on the matrix; The updating mechanism of knowledge granularity under the condition of change in the object set was explored, based on which the attribute importance was represented by knowledge granularity, and an incremental attribute reduction algorithm was constructed with the attribute importance as the heuristic information. The experiments of the incremental attribute reduction algorithm were implemented on 6 selected UCI datasets. The experimental results demonstrated that the incremental attribute reduction method consumed less time than the non-incremental method without compromising the accuracy of the reduction results, indicating that the incremental method was more efficient.

Key words: attribute reduction, tolerance degree, interval-valued decision-making information systems, knowledge granularity, incremental learning

CLC Number: 

  • TP391
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