计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 12-25.doi: 10.11896/jsjkx.210700111

• 数据库&大数据&数据科学* 上一篇    下一篇

监督和半监督学习下的多标签分类综述

武红鑫, 韩萌, 陈志强, 张喜龙, 李慕航   

  1. 北方民族大学计算机科学与工程学院 银川 750021
  • 收稿日期:2021-07-11 修回日期:2021-12-10 发布日期:2022-08-02
  • 通讯作者: 韩萌(2003051@nmu.edu.cn)
  • 作者简介:(893319518@qq.com)
  • 基金资助:
    国家自然科学基金(62062004);宁夏自然科学基金(2020AAC03216)

Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning

WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang   

  1. School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China
  • Received:2021-07-11 Revised:2021-12-10 Published:2022-08-02
  • About author:WU Hong-xin,born in 1998,bachelor.Her main research interests include data mining and so on.
    HAN Meng,born in 1982,Ph.D,professor,master’s supervisor.Her main research interests include data mining and so on.
  • Supported by:
    National Natural Science Foundation of China(62062004) and Ningxia Natural Science Foundation Project(2020AAC03216).

摘要: 传统的多标签分类算法大多数采用监督学习的方式,但现实生活中有许多数据没有被标记。通过人工的方式对需要的全部数据进行标记耗费的成本较高。半监督学习算法可以使用大量未标记数据和标记数据来进行工作,因此受到了人们的重视。文中首次从监督和半监督学习的角度对多标签分类算法进行阐述,同时全面地对多标签分类算法的应用领域进行了总结。从决策树、贝叶斯、支持向量机、神经网络和集成等多个方向对标签非相关性和标签相关性的监督学习算法进行概述,从批处理和在线的方向对半监督学习算法进行综述,从图像分类、文本分类和其他等角度对多标签的实际应用领域进行介绍。文中还简要分析了多标签的评估指标,最后给出了关于半监督学习下的复杂概念漂移处理、特征选择处理、标签复杂相关性处理和类不平衡处理的研究方向。

关键词: 半监督学习, 多标签分类, 监督学习, 图像分类, 文本分类

Abstract: Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of unlabeled data and labeled data,so they have received more attention from people.For the first time,multi-label classification algorithms are explained from the perspective of supervised learning and semi-supervised learning,and application fields of multi-label classification algorithms are comprehensively summarized.Among them,supervised learning algorithms of label non-correlation and label correlation are described in terms of decision trees,Bayesian,support vector machines,neural networks,and ensemble,semi-supervised learning algorithms are summarized from the perspectives of batch and online learning.The real-world application areas are introduced from the perspectives of image classification,text classification and other fields.Secondly,this paper briefly introduces evaluation metrics of multi-label.Finally,research directions of complex concept drift under semi-supervised learning,feature selection,complex correlation of labels and class imbalance are given.

Key words: Image classification, Multi-label classification, Semi-supervised learning, Supervised learning, Text classification

中图分类号: 

  • TP391
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