A Review on Multi-Label Learning Algorithms

作者：Min-Ling Zhang, Zhi‐Hua Zhou · 发表于：IEEE Transactions on Knowledge and Data Engineering · 年份：2013 · DOI：10.1109/tkde.2013.39 · 被引用次数：3339 · 研究领域：Text and Document Classification Technologies、Spam and Phishing Detection、Algorithms and Data Compression

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.