*** Under Construction ***
A Review on Multi-Label Learning Algorithms (Link) (2013)
A Review of Multi-Label Classification Methods (Link)
A systematic review of multi-label feature selection and a new method based on label construction (Link) (2016)
Binary relevance for multi-label learning: an overview (Link) (2018)
A Review on Dimensionality Reduction for Multi-Label Classification (Link) (2019)
A Survey on Multi-Label Data Stream Classification (Link) (2020)
The Emerging Trends of Multi-Label Learning (Link) (2021)
A review of methods for imbalanced multi-label classification (Link) (2021)
AutoML for Multi-Label Classification: Overview and Empirical Evaluation (Link) (2021)
A Survey on Extreme Multi-label Learning (Link) (2022)
Comprehensive Comparative Study of Multi-Label Classification Methods (Link) (2022)
A survey of multi-label classification based on supervised and semi-supervised learning (Link) (2023)
Application of Label Correlation in Multi-Label Classification: A Survey (Link) (2024)
Deep Learning for Multi-Label Learning: A Comprehensive Survey (Link) (2024)
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends (Link) (2024)
A Systematic Literature Review on Multi-label Data Stream Classification (Link) (2025)
A Survey on Evolutionary Feature Selection in Multilabel Classification (Link) (2026)
Label-Specific Feature Learning for Multi-Label Classification: A Survey (Link) (2026)
Binary Relevance, ML-KNN, Classifier Chain, RAkel, LiFT and many more....
Min-Ling Zhang (Google Scholar)
Zhi-Hua Zhou (Google Scholar)
Grigorios Tsoumakas (Google Scholar)
Jesse Read (Google Scholar)
Eleftherios Spyromitros-Xioufis (Google Scholar)
Gjorgji Madjarov (Google Scholar)
AI Paper Collector: https://github.com/MLNLP-World/AI-Paper-Collector
ACL Anthology: https://aclanthology.org
Semantic Scholar: https://www.semanticscholar.org
Google Scholar: https://scholar.google.com
Bing Academic: https://cn.bing.com/academic
Connected Papers: https://www.connectedpapers.com
Papers with Code: https://paperswithcode.com
Research Papers: https://papers.labml.ai/conferences
Deeplearning Monitor: https://deeplearn.org
OpenReview: https://openreview.net
OpenResearch: https://www.openresearch.org