Multi-label Classification (MLC)

Survey and Review Papers on MLC and co-related problems 

• 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) 

Active Research Labs

• PAttern Learning and Mining(PALM) Lab (Website)

• LAMDA (Learning And Mining from DatA) Lab (Website)

• Machine Learning and Knowledge Discovery (MLKD) group (Website)

• KDIS (Knowledge Discovery and Intelligent Systems) Lab (Website)

Popular Code Availability

• Some codes are available in PALM Lab (Link)

• Some codes are available in Lamda Lab (Link)

Dataset Availability

• Multi-Label Classification Dataset Repository by KDIS Lab (Website)

• MLC Dataset Catalog with many meta features (Website)

• RUMDR - R Ultimate Multilabel Dataset Repository for R Language (Website)

• Cometa is an exhaustive collection of multilabel datasets (Website)

• MLC Dataset Catalog (Website)

Tools Availability

• MEKA (Website)

• MLDA - Multi-Label Dataset Analyzer (Website)

• Mulan: A Java Library for Multi-Label Learning (Website)

• Multi-Label Classification in Python (Website)

• The utiml Package: Multi-label Classification in R (Website) 

• MLC Toolbox for Matlab (Website)

Base-line MLC Models

• Binary Relevance, ML-KNN, Classifier Chain, RAkel, LiFT and many more.... 

PhD Thesis on MLC

• GENETIC PROGRAMMING FOR MULTI-LABEL CLASSIFICATION (Website)

• DISTRIBUTED MULTI-LABEL LEARNING ON APACHE SPARK (Website)

• MULTI-LABEL CLASSIFICATION MODELS FOR HETEROGENEOUS DATA: AN ENSEMBLE-BASED APPROACH (Website)

Notable Researchers of MLC

• 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)

Related Resources

• 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