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Title: Categorizing feature selection methods for multi-label classification.
Authors: Pereira, Rafael Barros
Plastino, Alexandre
Zadrozny, Bianca
Merschmann, Luiz Henrique de Campos
Keywords: Multi-label learning
Feature selection
Data mining
Issue Date: 2016
Citation: 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: <>. Acesso em: 16 jan. 2018.
Abstract: 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.
ISSN: 1573-7462
Appears in Collections:DECOM - Artigos publicados em periódicos

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