《计算机应用研究》|Application Research of Computers

半监督特征选择综述

Survey of semi-supervised feature selection methods

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作者 张东方,陈海燕,王建东
机构 南京航空航天大学 a.计算机科学与技术学院;b.软件新技术与产业化协同创新中心,南京 211100
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文章编号 1001-3695(2021)02-001-0321-09
DOI 10.19734/j.issn.1001-3695.2020.01.0001
摘要 如何针对半监督数据集,利用不完整的监督信息完成特征选择,已经成为模式识别与机器学习领域的研究热点。为方便研究者系统地了解半监督特征选择领域的研究现状和发展趋势,对半监督特征选择方法进行综述。首先探讨了半监督特征选择方法的分类,将其按理论基础的不同分为基于图的方法、基于伪标签的方法、基于支持向量机的方法以及其他方法;然后详细介绍并比较了各个类别的典型方法;之后整理了半监督特征选择的热点应用;最后展望了半监督特征选择方法未来的研究方向。
关键词 机器学习; 半监督学习; 特征选择
基金项目 中央高校基本科研业务费专项资金资助项目(NS2019054)
本文URL http://www.arocmag.com/article/01-2021-02-001.html
英文标题 Survey of semi-supervised feature selection methods
作者英文名 Zhang Dongfang, Chen Haiyan, Wang Jiandong
机构英文名 a.School of Computer Science & Technology,b.Collaborative Innovation Center of Novel Software Technology & Industrialization,Nanjing University of Aeronautics & Astronautics,Nanjing 211100,China
英文摘要 How to select features on semi-supervised data sets by incomplete supervisory information has become a research hotspot in the field of pattern recognition and machine learning. In order to facilitate researchers to systematically understand the research status and development trend of semi-supervised feature selection, this paper reviewed the semi-supervised feature selection methods. This paper first discussed the classification of semi-supervised feature selection methods and divided them into graph-based methods, pseudo-label-based methods, SVM-based methods and other methods according to their theoretical basis, then introduced and compared typical methods for each category, and then sorted out hot applications of semi-supervised feature selection. Finally, this paper looked forward to the future research directions of semi-supervised feature selection.
英文关键词 machine learning; semi-supervised learning; feature selection
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收稿日期 2020/1/8
修回日期 2020/3/5
页码 321-329
中图分类号 TP391.4
文献标志码 A