Computer Science ›› 2022, Vol. 49 ›› Issue (8): 12-25.doi: 10.11896/jsjkx.210700111

• Database & Big Data & Data Science • Previous Articles     Next Articles

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).

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

CLC Number: 

  • TP391
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