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

• Published in: Ji suan ji ke xue Vol. 49; no. 8; pp. 12 - 25
• Main Authors: Wu, Hong-xin, Han, Meng, Chen, Zhi-qiang, Zhang, Xi-long, Li, Mu-hang
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.08.2022; Editorial office of Computer Science
• Subjects: Algorithms; Classification; Correlation; Decision trees; Distance learning; Image classification; Machine learning; Neural networks; Semi-supervised learning; supervised learning|semi-supervised learning|multi-label classification|image classification|text classification; Support vector machines
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210700111

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