基于信息熵加权的粒球邻域粗糙集属性约简方法

曾诗淇; 刘盾

doi:10.16152/j.cnki.xdxbzr.2026-01-012

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基于信息熵加权的粒球邻域粗糙集属性约简方法

粗糙集与形式概念分析中的约简研究 | 更新时间：2026-02-24

- 基于信息熵加权的粒球邻域粗糙集属性约简方法
- IEWGBNRS: A novel attribute reduction approach based on information entropy-weighted granular-ball neighborhood rough set
- 西北大学学报（自然科学版） 2026年56卷第1期页码：128-140
- 作者机构：
  
  1.西南交通大学　计算机与人工智能学院，四川　成都　611756
  2.西南交通大学　经济管理学院，四川　成都　610031
- 作者简介：
  
  曾诗淇，女，硕士研究生，从事粒计算与粗糙集理论及其应用研究，zengshiqi9@163.com。
  刘盾，男，教授，博士生导师，从事粒计算与人工智能研究，newton83@163.com。
- 基金信息：
  
  国家自然科学基金(62276217;62402424);中央高校基本科研业务费项目(2682024KJ005;2682024ZTPY021)
- DOI：10.16152/j.cnki.xdxbzr.2026-01-012
  中图分类号： TP181
- 收稿：2025-07-10，
  
  修回：2025-08-15，
  
  纸质出版：2026-02-25
- 稿件说明：
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曾诗淇, 刘盾. 基于信息熵加权的粒球邻域粗糙集属性约简方法[J]. 西北大学学报（自然科学版）, 2026,56(1):128-140.

ZENG Shiqi, LIU Dun. IEWGBNRS: A novel attribute reduction approach based on information entropy-weighted granular-ball neighborhood rough set[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(1): 128-140.
曾诗淇, 刘盾. 基于信息熵加权的粒球邻域粗糙集属性约简方法[J]. 西北大学学报（自然科学版）, 2026,56(1):128-140. DOI： 10.16152/j.cnki.xdxbzr.2026-01-012.

ZENG Shiqi, LIU Dun. IEWGBNRS: A novel attribute reduction approach based on information entropy-weighted granular-ball neighborhood rough set[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(1): 128-140. DOI： 10.16152/j.cnki.xdxbzr.2026-01-012.

摘要

粒球邻域粗糙集模型是一种融合粒计算与邻域关系的改进型粗糙集模型，能够有效处理连续值数据。然而，现有模型在属性权重不一致的情况下难以合理评估各属性的重要性，导致关键属性在约简过程中易被误删。为解决该问题，提出了一种基于信息熵加权的粒球邻域粗糙集属性约简方法。首先，结合属性内部分布特征与条件属性和决策属性之间的相关性，对属性赋予权重；其次，构建基于加权粒球邻域关系的改进模型，并定义相应的属性约简方法；最后，基于粒球平均纯度实现属性约简过程。实验在多个UCI数据集上进行，结果表明：该方法不仅具有较高的属性约简率（最高可达76.92%），还能显著提升支持向量机（SVM）和K近邻（KNN）分类器的平均准确率，分别提高1.57%和1.58%；此外，该方法在处理高维复杂数据时表现出更强的适应性和稳定性，具有良好的应用前景。

Abstract

The granular ball neighborhood rough set model is an improved rough set model that integrates granular computing and neighborhood relations

capable of effectively handling continuous-valued data. However

existing models struggle to reasonably evaluate attribute importance under inconsistent attribute weights

leading to the risk of mistakenly removing key attributes during attribute reduction. To address this issue

we propose an attribute reduction algorithm for the granular ball neighborhood rough set model based on information entropy weighting. First

attribute weights are assigned by combining the internal distribution characteristics of attributes with the correlation between conditional and decision attributes. Second

an improved model based on the weighted granular ball neighborhood relation is constructed

and the corresponding attribute reduction method is defined. Finally

attribute reduction is implemented based on the average purity of granular balls. Extensive experiments are conducted on multiple UCI datasets. The results show that the proposed method not only achieves a high attribute reduction rate (up to 76.92%)

but also significantly improves the average classification accuracy of classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN)

increasing by 1.57% and 1.58%

respectively. Moreover

the method demonstrates stronger adaptability and stability in processing high-dimensional and complex datasets

indicating promising application potential.

关键词

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