Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/50179
Título: A lazy feature selection method for multi-label classification
Palavras-chave: Multi-label classification
Data mining
Feature selection
Data do documento: 26-Jan-2021
Editor: IOS Press
Citação: PEREIRA, R. B. et al. A lazy feature selection method for multi-label classification. Intelligent Data Analysis, [S.l.], v. 25, n. 1, p. 21-34, Jan. 2021. DOI: 10.3233/IDA-194878.
Resumo: In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.
URI: https://content.iospress.com/articles/intelligent-data-analysis/ida194878
http://repositorio.ufla.br/jspui/handle/1/50179
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