Categorizing feature selection methods for multi-label classification.
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2016
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Resumo
In many important application domains such as text categorization, biomolecular
analysis, scene classification and medical diagnosis, examples are naturally associated with
more than one class label, giving rise to multi-label classification problems. This fact has led,
in recent years, to a substantial amount of research on feature selection methods that allow
the identification of relevant and informative features for multi-label classification. However,
the methods proposed for this task are scattered in the literature, with no common framework
to describe them and to allow an objective comparison. Here, we revisit a categorization
of existing multi-label classification methods and, as our main contribution, we provide a
comprehensive survey and novel categorization of the feature selection techniques that have
been created for the multi-label classification setting. We conclude this work with concrete
suggestions for future research in multi-label feature selection which have been derived from
our categorization and analysis.
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Multi-label learning, Feature selection, Classification, Data mining
Citação
PEREIRA, R. B. et al. Categorizing feature selection methods for multi-label classification. Artificial Intelligence Review, Dordrecht, v. 1, p. 1-22, 2016. Disponível em: <https://link.springer.com/article/10.1007/s10462-016-9516-4>. Acesso em: 16 jan. 2018.