Application of Label Correlation in Multi-Label Classification: A Survey

被引:0
作者
Huang, Shan [1 ,2 ]
Hu, Wenlong [1 ,3 ]
Lu, Bin [1 ,4 ]
Fan, Qiang [1 ,2 ]
Xu, Xinyao [1 ,2 ]
Zhou, Xiaolei [1 ,2 ]
Yan, Hao [1 ,2 ]
机构
[1] Natl Univ Def Technol, Res Inst 63, Nanjing 210007, Peoples R China
[2] Natl Univ Def Technol, Lab Big Data & Decis, Nanjing 410072, Peoples R China
[3] Nanjing Univ Informat Sci Technol, Sch Elect & Informat Engn, Nanjing 210044, Peoples R China
[4] Nanjing Univ Informat Sci & Technol, Sch Software, Nanjing 210044, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 19期
基金
中国国家自然科学基金;
关键词
multi-label classification; label correlation; label relationship; deep learning; GRAPH; NETWORKS;
D O I
10.3390/app14199034
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-label classification, each instance in Multi-Label Classification corresponds to multiple labels, and there is a correlation between these labels, which contains a wealth of information. Therefore, the ability to effectively mine and utilize the complex correlations between labels has become a key factor in Multi-Label Classification methods. In recent years, research on label correlations has shown a significant growth trend internationally, reflecting its importance. Given that, this paper presents a survey on the label correlations in Multi-Label Classification to provide valuable references and insights for future researchers. The paper introduces multi-label datasets across various fields, elucidates and categorizes the concept of label correlations, emphasizes their utilization in Multi-Label Classification and associated subproblems, and provides a prospect for future work on label correlations.
引用
收藏
页数:22
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