Journal
INFORMATION FUSION
Volume 44, Issue -, Pages 33-45Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2017.12.001
Keywords
Multi-label classification; Ensemble methods
Funding
- Spanish Ministry of Economy and Competitiveness [TIN-2014-55252-P]
- FEDER funds
- Spanish Ministry of Education [FPU15/02948]
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The great attention given by the scientific community to multi-label learning in recent years has led to the development of a large number of methods, many of them based on ensembles. A comparison of the state-of-theart in ensembles of multi-label classifiers over a wide set of 20 datasets have been carried out in this paper, evaluating their performance based on the characteristics of the datasets such as imbalance, dependence among labels and dimensionality. In each case, suggestions are given to choose the algorithm that fits best. Further, given the absence of taxonomies of ensembles of multi-label classifiers, a novel taxonomy for these methods is proposed.
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