计算机应用与软件2025，Vol.42Issue(7)：1-11,32,12.DOI:10.3969/j.issn.1000-386x.2025.07.001
深度特征选择方法研究综述
REVIEW RESEARCH ON DEEP FEATURE SELECTION METHODS
摘要
Abstract
Feature selection can eliminate noise and redundant information in data,simplify computational complexity and data analysis difficulty,so it has significant research value in data mining and machine learning.With the development of deep learning technology,deep neural networks have been applied to feature selection and achieved better results than traditional methods.Still,there is a lack of comprehensive description and discussion of such research.In this paper,we described the traditional feature selection algorithms,and summarized the research progress of deep feature selection algorithms into two categories:input-layer embedding and encoding-layer embedding.The effects of typical algorithms were tested on public datasets,and challenging and research directions were further discussed.关键词
特征选择/数据挖掘/深度学习/深度特征选择Key words
Feature selection/Data mining/Deep learning/Deep feature selection分类
信息技术与安全科学引用本文复制引用
陈挺,刘香君,臧璇,池明旻..深度特征选择方法研究综述[J].计算机应用与软件,2025,42(7):1-11,32,12.基金项目
国家自然科学基金项目(62171139). （62171139）
