Abstract
Multi-label learning is an important machine learning setting where each example is associated with multiple class labels simultaneously. Firstly, definition, motivation and background, and learning system structure for multi-label learning are introduced. Secondly, multi-label evaluation measures and the issue of label correlation are discussed. Thirdly, basic ideas and technical details on four representative multi-label learning algorithms are considered. Lastly, theory, extensions, and future challenges on multi-label learning are introduced.
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Zhou, ZH., Zhang, ML. (2017). Multi-label Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_910
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_910
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