Feature selection based on information entropy with variable precision fuzzy mixed granularity

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Intelligence Pub Date : 2025-10-04 DOI:10.1007/s10489-025-06890-8

Jiaxin Wang, Jingqian Wang, Xiaohong Zhang, Jun Liu

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引用次数: 0

Abstract

Fuzzy rough set theory allows defining different fuzzy relationships for different attribute types to quantify the similarity between objects. Meanwhile, information entropy, a powerful tool for quantifying uncertainty, is further extended within this framework to fuzzy rough set-based information entropy. The granularity division of traditional fuzzy entropy usually relies on fuzzy similarity relationships. In this paper, we first define variable precision mixed fuzzy granularity, combine it with fuzzy entropy to construct the information entropy based on variable precision mixed fuzzy granularity, and define fuzzy granularity entropy (FGe), fuzzy granularity joint entropy (FGJe), fuzzy granularity conditional entropy (FGCe), and fuzzy granularity mutual information (FGMI), and study the relationship and related properties among them. Then the importance function for evaluating the importance of features is constructed using FGMI, which lays the foundation for the feature selection (FS) algorithm. To evaluate the performance of the algorithm, numerical experiments are conducted on 15 public datasets and compared with other algorithms. The experimental results show that the method shows good adaptability and FS ability for handling different types of data.

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基于变精度模糊混合粒度信息熵的特征选择

模糊粗糙集理论允许对不同的属性类型定义不同的模糊关系来量化对象之间的相似度。同时，在此框架下，将信息熵这一量化不确定性的有力工具进一步扩展为基于模糊粗糙集的信息熵。传统模糊熵的粒度划分通常依赖于模糊相似关系。本文首先定义了变精度混合模糊粒度，并将其与模糊熵相结合，构建了基于变精度混合模糊粒度的信息熵，定义了模糊粒度熵（FGe）、模糊粒度联合熵（FGJe）、模糊粒度条件熵（FGCe）和模糊粒度互信息（FGMI），并研究了它们之间的关系和相关性质。然后利用FGMI构造了评价特征重要性的重要函数，为特征选择（FS）算法奠定了基础。为了评估算法的性能，在15个公开数据集上进行了数值实验，并与其他算法进行了比较。实验结果表明，该方法对处理不同类型的数据具有良好的适应性和FS能力。

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来源期刊

Applied Intelligence 工程技术-计算机：人工智能

CiteScore

6.60

自引率

20.80%

发文量

1361

审稿时长

5.9 months

期刊介绍： With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.