Feature selection method for dynamic feature interaction in fuzzy neighborhood system

XU Jiucheng; NIU Wulin; DUAN Jianghao; ZHANG Shan; BAI Qing

doi:10.16152/j.cnki.xdxbzr.2025-02-009

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Feature selection method for dynamic feature interaction in fuzzy neighborhood system

更新时间：2025-04-21

- Feature selection method for dynamic feature interaction in fuzzy neighborhood system
- Journal of Northwest University (Natural Science Edition) Vol. 55, Issue 2, Pages: 320-332(2025)
- 作者机构：
  
  1.河南师范大学　计算机与信息工程学院，河南　新乡　 453007
  2.智慧商务与物联网技术河南省工程实验室，河南　新乡　 453007
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2025-02-009
  CLC： TP181
- Received：28 September 2024，
  
  Published：25 April 2025
- 稿件说明：
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XU Jiucheng, NIU Wulin, DUAN Jianghao, et al. Feature selection method for dynamic feature interaction in fuzzy neighborhood system[J]. Journal of northwest university (natural science edition), 2025, 55(2): 320-332.
DOI：

XU Jiucheng, NIU Wulin, DUAN Jianghao, et al. Feature selection method for dynamic feature interaction in fuzzy neighborhood system[J]. Journal of northwest university (natural science edition), 2025, 55(2): 320-332. DOI： 10.16152/j.cnki.xdxbzr.2025-02-009.

摘要

针对大部分基于模糊邻域信息系统的特征选择方法，在选入特征时只是单调的按照特征顺序进行特征交互的问题，以及大多数特征度量函数仅从代数观或信息观的视角构造度量函数的缺陷，提出了一种基于模糊邻域动态特征交互的特征选择方法。首先，引入模糊邻域互信息计算特征相关度，并根据特征相关度将特征顺序重组。其次，分析特征动态交互的过程，通过模糊邻域互信息以及模糊邻域条件互信息，按照特征排序依次计算特征之间的冗余及动态交互程度。最后，为改善大多数特征度量函数构造视角单一的缺陷，提出一种多视角下的依赖模糊邻域混合互信息度量函数。在8个公共数据集上与现有的7种属性约简算法进行对比实验，实验结果表明，该算法消除了冗余特征，同时提高了数据分类的准确度。

Abstract

In order to solve the problem that most feature selection methods based on fuzzy neighborhood information system only monotonically interact with features according to the order of features when selecting features

and that most feature measurement functions only construct metric functions from the perspective of algebraic view or information view

a feature selection method based on dynamic feature interaction in fuzzy neighborhood is proposed. Firstly

fuzzy neighborhood mutual information is introduced to calculate the feature correlation degree and reorganize the feature order according to the feature correlation degree. Secondly

the process of dynamic interaction between features is analyzed

and the degree of redundancy and dynamic interaction between features is calculated according to the order of features through fuzzy neighborhood mutual information and fuzzy neighborhood conditional mutual information. Finally

in order to improve the defect of single perspective in the construction of most feature metric functions

a hybrid mutual information metric function relying on fuzzy neighborhoods from multiple perspectives is proposed. Experimental results show that the proposed algorithm eliminates redundant features and improves the accuracy of data classification while comparing with seven existing feature reduction algorithms on eight public datasets.

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references

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