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Título : | Categorizing feature selection methods for multi-label classification. |
Autor : | Pereira, Rafael Barros Plastino, Alexandre Zadrozny, Bianca Merschmann, Luiz Henrique de Campos |
Palabras clave : | Multi-label learning Feature selection Classification Data mining |
Fecha de publicación : | 2016 |
Citación : | 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. |
Resumen : | 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. |
URI : | http://www.repositorio.ufop.br/handle/123456789/9367 |
metadata.dc.identifier.uri2: | https://link.springer.com/article/10.1007/s10462-016-9516-4 |
metadata.dc.identifier.doi: | https://doi.org/10.1007/s10462-016-9516-4 |
ISSN : | 1573-7462 |
Aparece en las colecciones: | DECOM - Artigos publicados em periódicos |
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Fichero | Descripción | Tamaño | Formato | |
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ARTIGO_CategorizingFatureSelection.pdf Restricted Access | 856,83 kB | Adobe PDF | Visualizar/Abrir |
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