知识粒度视角下区间值决策信息系统的增量式属性约简

doi:10.6040/j.issn.1672-3961.0.2024.111

摘要/Abstract

摘要： 鉴于以区间值决策信息系统为对象的非增量式属性约简效率不高,现将知识粒度的概念推广到区间值决策信息系统中,从知识粒度的视角系统地研究区间值决策信息系统中的增量式属性约简方法。在区间值决策信息系统中引入相容度的概念并由此改进区间值相容度的度量方法;根据相容度确定相容关系并构造出相应的相容关系矩阵,以此得出一种基于矩阵的区间值决策信息系统中知识粒度的计算方法;探讨在对象集发生变化条件下知识粒度的更新机制,在此基础上用知识粒度来表示属性重要度,以属性重要度为启发式信息构建出增量式属性约简算法。在6个精选的UCI数据集上实施增量式属性约简算法的试验,试验结果表明:在不影响属性约简结果精度条件下,增量式属性约简方法较非增量式属性约简方法消耗时间更少,增量式属性约简方法更加高效。

关键词: 属性约简, 相容度, 区间值决策信息系统, 知识粒度, 增量学习

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

中图分类号:

TP391

邱利芹,王磊,于越,孙雅慧. 知识粒度视角下区间值决策信息系统的增量式属性约简[J]. 山东大学学报 (工学版), 2025, 55(6): 45-57.

QIU Liqin, WANG Lei, YU Yue, SUN Yahui. Incremental attribute reduction of interval-valued decision-making information systems from the perspective of knowledge granularity[J]. Journal of Shandong University(Engineering Science), 2025, 55(6): 45-57.

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