计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (2): 165-172.doi: 10.11896/jsjkx.231100202

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于模糊邻域相对决策熵的属性约简算法

徐久成, 张杉, 白晴, 马妙贤   

  1. 河南师范大学计算机与信息工程学院 河南 新乡 453007
    河南师范大学智慧商务与物联网技术河南省工程实验室 河南 新乡 453007
  • 收稿日期:2023-11-30 修回日期:2024-06-07 出版日期:2025-02-15 发布日期:2025-02-17
  • 通讯作者: 张杉(2208183014@stu.htu.edu.cn)
  • 作者简介:(xjc@htu.edu.cn)
  • 基金资助:
    国家自然科学基金(61976082,62076089,62002103)

Attribute Reduction Algorithm Based on Fuzzy Neighborhood Relative Decision Entropy

XU Jiucheng, ZHANG Shan, BAI Qing, MA Miaoxian   

  1. College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
    Engineering Lab of Intelligence Business & Internet of Things,Henan Province,Henan Normal University,Xinxiang,Henan 453007,China
  • Received:2023-11-30 Revised:2024-06-07 Online:2025-02-15 Published:2025-02-17
  • About author:XU Jiucheng,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include granular computing,data mining and bioinformatics.
    ZHANG Shan,born in 1998,postgra-duate.Her main research interests include data mining and fuzzy rough set.
  • Supported by:
    National Natural Science Foundation of China(61976082,62076089,62002103).

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摘要： 针对模糊邻域粗糙集对数据分布敏感且无法有效评估密度差异较大数据集的分类不确定性这一问题,提出了一种基于模糊邻域相对决策熵的属性约简算法。首先,采用相对距离定义样本的分类不确定度,重塑模糊邻域相似关系,并结合变精度模糊邻域粗糙近似,减少样本被归入错误类别的可能性;其次,在信息观下将模糊邻域可信度和覆盖度引入信息熵,并与基于代数观构造的模糊邻域相对依赖度相结合,提出模糊邻域相对决策熵;最后,设计一种基于模糊邻域相对决策熵的属性约简算法,从信息观和代数观两个角度来评估属性的重要度。在8个公共数据集上将其与现有的6种属性约简算法进行对比实验,结果表明,所提算法能有效地测量不同数据分布下样本的不确定度,提高数据的分类性能。

关键词: 属性约简, 模糊邻域粗糙集, 分类不确定度, 信息熵

Abstract: Aiming at the problem that fuzzy neighborhood rough set is sensitive to data distribution and cannot effectively evaluate the classification uncertainty of datasets with large density differences,this paper proposes an attribute reduction algorithm based on fuzzy neighborhood relative decision entropy.Firstly,the classification uncertainty of the sample is defined by using the relative distance,thus remodeling the fuzzy neighborhood similarity relationship.Combined with the variable precision fuzzy neighborhood rough approximation,the possibility of the sample being classified into the wrong category is reduced.Secondly,the information entropy is augmented with the fuzzy neighborhood credibility and coverage under the information view,and this is integrated with the fuzzy neighborhood relative dependence constructed based on the algebraic view to introduce the fuzzy neighborhood relative decision entropy.Finally,an attribute reduction algorithm based on the fuzzy neighborhood relative decision entropy is designed to evaluate the importance of attributes from both the information and algebraic viewpoints.Comparative experiments with six existing attribute reduction algorithms on eight public datasets show that the proposed algorithm can effectively measure the uncertainty of samples under different data distributions and improve the classification performance of data.

Key words: Attribute reduction, Fuzzy neighborhood rough set, Classification uncertainty, Information entropy

中图分类号: 

  • TP181
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