Categorizing feature selection methods for multi-label classification

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorPereira, Rafael B.-
Autor(es): dc.creatorPlastino, Alexandre-
Autor(es): dc.creatorZadrozny, Bianca-
Autor(es): dc.creatorMerschmann, Luiz H. C.-
Data de aceite: dc.date.accessioned2026-02-09T12:40:25Z-
Data de disponibilização: dc.date.available2026-02-09T12:40:25Z-
Data de envio: dc.date.issued2019-05-16-
Data de envio: dc.date.issued2019-05-16-
Data de envio: dc.date.issued2018-01-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/34292-
Fonte completa do material: dc.identifierhttps://link.springer.com/article/10.1007/s10462-016-9516-4-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1166006-
Descrição: dc.descriptionIn 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.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceArtificial Intelligence Review-
Palavras-chave: dc.subjectMulti-label learning-
Palavras-chave: dc.subjectFeature selection-
Palavras-chave: dc.subjectData mining-
Título: dc.titleCategorizing feature selection methods for multi-label classification-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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