IEWGBNRS: A novel attribute reduction approach based on information entropy-weighted granular-ball neighborhood rough set

ZENG Shiqi; LIU Dun

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

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IEWGBNRS: A novel attribute reduction approach based on information entropy-weighted granular-ball neighborhood rough set

更新时间：2026-02-24

- IEWGBNRS: A novel attribute reduction approach based on information entropy-weighted granular-ball neighborhood rough set
- Journal of Northwest University (Natural Science Edition) Vol. 56, Issue 1, Pages: 128-140(2026)
- 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：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2026-01-012
  CLC： TP181
- Received：10 July 2025，
  
  Revised：2025-08-15，
  
  Published：25 February 2026
- 稿件说明：
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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：

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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references

PAWLAK Z . Rough sets [J ] . International Journal of Computer & Information Sciences , 1982 , 11 ( 5 ): 341 - 356 .

桑彬彬 , 徐伟华 . 直觉模糊序决策信息系统的分布约简 [J ] . 模糊系统与数学 , 2017 , 31 ( 2 ): 164 - 170 .

SANG B B , XU W H . Distribution reduction in an intuitionistic fuzzy ordered decision information system [J ] . Fuzzy Systems and Mathematics , 2017 , 31 ( 2 ): 164 - 170 .

GARDINER E J , GILLET V J . Perspectives on knowledge discovery algorithms recently introduced in chemoinformatics: Rough set theory, association rule mining, emerging patterns, and formal concept analysis [J ] . Journal of Chemical Information and Modeling , 2015 , 55 ( 9 ): 1781 - 1803 .

PETROSINO A , SALVI G . Rough fuzzy set based scale space transforms and their use in imageanalysis [J ] . International Journal of Approximate Reasoning , 2006 , 41 ( 2 ): 212 - 228 .

EMAM W , AHMMAD J , MAHMOOD T , et al . Classification of artificial intelligence tools for civil engineering under the notion of complex fuzzy rough Frank aggregation operators [J ] . Scientific Reports , 2024 , 14 ( 1 ): 11892 .

EL-BABLY M K , ABU-GDAIRI R , EL-GAYAR M A . Medical diagnosis for the problem of Chikungunya disease using soft rough sets [J ] . AIMS Mathematics , 2023 , 8 ( 4 ): 9082 - 9105 .

HU Q H , YU D R , LIU J F , et al . Neighborhood rough set based heterogeneous feature subset selection [J ] . Information Sciences , 2008 , 178 ( 18 ): 3577 - 3594 .

SHEEJA T K , KURIAKOSE A S . A novel feature selection method using fuzzy rough sets [J ] . Computers in Industry , 2018 , 97 : 111 - 116 .

YUAN K H , XU W H , MIAO D Q . A local rough set method for feature selection by variable precision composite measure [J ] . Applied Soft Computing , 2024 , 155 : 111450 .

YU Y , WAN M , QIAN J , et al . Feature selection for multi-label learning based on variable-degree multi-granulation decision-theoretic rough sets [J ] . International Journal of Approximate Reasoning , 2024 , 169 : 109181 .

ZHANG L , BAI J C , SUN B Z , et al . Kernel multi-granularity double-quantitative rough set based on ensemble empirical mode decomposition: Application to stock price trends prediction [J ] . International Journal of Approximate Reasoning , 2024 , 172 : 109217 .

WANG H , JU Y B , PORCEL C . Energy Internet project evaluation in circular economy practices: A novel multi-criteria decision-making framework with flexible linguistic expressions based on multi-granularity cloud-rough set [J ] . Computers & Industrial Engineering , 2025 , 201 : 110890 .

徐伟华 , 刘士虎 , 张文修 . 一般二元关系下基于粗糙隶属函数的程度粗糙集 [J ] . 重庆理工大学学报(自然科学版) , 2010 , 24 ( 10 ): 101 - 108 .

XU W H , LIU S H , ZHANG W X . The graded rough set based on membership function in general binary relation [J ] . Journal of Chongqing University of Technology , 2010 , 24 ( 10 ): 101 - 108 .

NIERADKA G , BUTKIEWICZ B . A method for automatic membership function estimation based on fuzzy measures [C ] // Foundations of Fuzzy Logic and Soft Computing . Berlin , Heidelberg : Springer , 2007 : 451 - 460 .

JENSEN R , CORNELIS C . A new approach to fuzzy-rough nearest neighbour classification [M ] // Rough Sets and Current Trends in Computing . Berlin, Heidelberg : Springer , 2008 : 310 - 319 .

张小红 , 裴道武 , 代建华 . 模糊数学与Rough集理论 [M ] . 北京 : 清华大学出版社 , 2013 : 222 - 226 .

YANG X , LI Y H , XIA S Y , et al . Granular-ball three-way decision [C ] // Rough Sets . Cham : Springer , 2023 : 283 - 295 .

XIA S Y , ZHANG H , LI W H , et al . GBNRS: A novel rough set algorithm for fast adaptive attribute reduction in classification [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 3 ): 1231 - 1242 .

SHANNON C E . A mathematical theory of communication [J ] . The Bell System Technical Journal , 1948 , 27 ( 3 ): 379 - 423 .

FAYYAD U M , IRANI K B . Multi-interval discretization of continuous-valued attributes for classification learning [C ] // International Joint Conference on Artificial Intelligence , 1993 , 93 ( 2 ): 1022 - 1029 .

WITTEN I H , FRANK E . Data mining: Practical machine learning tools and techniques with Java implementations [J ] . Acm Sigmod Record , 2002 , 31 ( 1 ): 76 - 77 .

XIA S Y , DAI X C , WANG G Y , et al . An efficient and adaptive granular-ball generation method in classification problem [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 ( 4 ): 5319 - 5331 .

CAO X M , YANG X , XIA S Y , et al . Open continual feature selection via granular-ball knowledge transfer [J ] . IEEE Transactions on Knowledge and Data Engineering , 2024 , 36 ( 12 ): 8967 - 8980 .

苗夺谦 , 胡桂荣 . 知识约简的一种启发式算法 [J ] . 计算机研究与发展 , 1999 , 36 ( 6 ): 681 - 684 .

MIAO D Q , HU G R . A heuristic algorithm for knowledge [J ] . Journal of Computer Research and Development , 1999 , 36 ( 6 ): 681 - 684 .

王国胤 , 于洪 , 杨大春 . 基于条件信息熵的决策表约简 [J ] . 计算机学报 , 2002 , 25 ( 7 ): 759 - 766 .

WANG G Y , YU H , YANG D C . Decision table reduction based on conditional information entropy [J ] . Chinese Journal of Computers , 2002 , 25 ( 7 ): 759 - 766 .

杨明 . 决策表中基于条件信息熵的近似约简 [J ] . 电子学报 , 2007 , 35 ( 11 ): 2156 - 2160 .

YANG M . Approximate reduction based on conditional information entropy in decision tables [J ] . Acta Electronic Sinica , 2007 , 35 ( 11 ): 2156 - 2160 .

杨传健 , 葛浩 , 汪志圣 . 基于粗糙集的属性约简方法研究综述 [J ] . 计算机应用研究 , 2012 , 29 ( 1 ): 16 - 20 .

YANG C J , GE H , WANG Z S . Overview of attribute reduction based on rough set [J ] . Application Research of Computers , 2012 , 29 ( 1 ): 16 - 20 .

VAN ERVEN T , HARREMOS P . Rényi divergence and kullback-leibler divergence [J ] . IEEE Transactions on Information Theory , 2014 , 60 ( 7 ): 3797 - 3820 .

JIN F F , GAO X X , ZHOU L G . Bounded rationality consensus reaching process with regret theory and weighted Moment estimation for multi-attribute group decision making [J ] . Information Fusion , 2025 , 115 : 102778 .

LI Y , WU X X , WANG X Z . Incremental reduction methods based on granular ball neighborhood rough sets and attribute grouping [J ] . International Journal of Approximate Reasoning , 2023 , 160 : 108974 .

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