计算机工程与应用2026，Vol.62Issue(11)：41-61,21.DOI:10.3778/j.issn.1002-8331.2507-0192
复杂数据背景下的多标记学习研究进展
Survey on Multi-Label Learning Under Complex Data Environment
摘要
Abstract
Multi-label learning is an important learning paradigm in the field of machine learning,which shows advanced performance in many applications.Data with the main characteristics of large-scale,incomplete information,multiple dimen-sions and dynamic evolution are bringing many challenges to multi-label learning.It is necessary to carry out a more refined design of the multi-label learning method according to the characteristics of different learning tasks under com-plex data environment.Therefore,this paper analyzes the source of the complexity of multi-label data and reviews the research progress of multi-label learning under complex data environment.Firstly,the paper introduces the problem defini-tion,learning form and development trend of multi-label learning in detail.Next,the complexity of multi-label data from three levels:label,feature and sample is discussed.Then,the summarized,elaborated and analyzed multi-label learning methods under complex data environment are provided.Finally,the challenges and the possible future application direc-tions of multi-label learning are summarized.关键词
多标记学习/标记复杂性/特征复杂性/样本复杂性Key words
multi-label learning/label complexity/feature complexity/sample complexity分类
信息技术与安全科学引用本文复制引用
杜国栋,王敖,路鹏伟,叶倩芝,张佳..复杂数据背景下的多标记学习研究进展[J].计算机工程与应用,2026,62(11):41-61,21.基金项目
河北省自然科学基金(F2025203073) （F2025203073）
河北省教育厅科学研究项目(QN2025001) （QN2025001）
燕山大学基础创新科研培育项目(2024LGQN004) （2024LGQN004）
河北省创新能力提升计划项目(22567626H) （22567626H）
中央高校基本科研业务费专项资金(21625110). （21625110）
