Multi-label learning: a review of the state of the art and ongoing research
Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up‐to‐date overview about multi‐label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented. WIREs Data Mining Knowl Discov 2014, 4:411–444. doi: 10.1002/widm.1139 This article is categorized under: Technologies > Classification Technologies > Machine Learning