A Review on Multi-Label Learning Algorithms

2013/05/31 by Min-Ling Zhang, Zhi-Hua Zhou, Zhi‐Hua Zhou · 31 citations
Computer Science · #Algorithms and Data Compression #Spam and Phishing Detection #Text and Document Classification Technologies

paper · doi:10.1109/tkde.2013.39

openalex publication_date 2013/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.

Citations

• Multi-instance multi-label learning
• Solving the multiple instance problem with axis-parallel rectangles
• The meaning and use of the area under a receiver operating characteristic (ROC) curve.

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