半监督特征选择综述  

Survey of semi-supervised feature selection methods

作　　者：张东方 陈海燕[1,2] 王建东[1] Zhang Dongfang;Chen Haiyan;Wang Jiandong(School of Computer Science&Technology,Nanjing University of Aeronautics&Astronautics,Nanjing 211100,China;Collaborative Innovation Center of Novel Software Technology&Industrialization,Nanjing University of Aeronautics&Astronautics,Nanjing 211100,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211100 [2]南京航空航天大学软件新技术与产业化协同创新中心,南京211100

出　　处：《计算机应用研究》2021年第2期321-329,共9页Application Research of Computers

基　　金：中央高校基本科研业务费专项资金资助项目(NS2019054)。

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。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.

关 键 词：机器学习 半监督学习 特征选择 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]