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Graphical feature selection for multilabel classification tasks

dc.contributor.authorLastra Madrid, Gerardo Jesús 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorBahamonde Rionda, Antonio 
dc.date.accessioned2013-01-30T10:16:15Z
dc.date.available2013-01-30T10:16:15Z
dc.date.issued2011
dc.identifier.citationLecture Notes in Computer Science, 7014, p. 246-257 (2011); doi:10.1007/978-3-642-24800-9_24spa
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10651/9949
dc.description.abstractMultilabel was introduced as an extension of multi-class classification to cope with complex learning tasks in different application fields as text categorization, video o music tagging or bio-medical labeling of gene functions or diseases. The aim is to predict a set of classes (called labels in this context) instead of a single one. In this paper we deal with the problem of feature selection in multilabel classification. We use a graphical model to represent the relationships among labels and features. The topology of the graph can be characterized in terms of relevance in the sense used in feature selection tasks. In this framework, we compare two strategies implemented with different multilabel learners. The strategy that considers simultaneously the set of all labels outperforms the method that considers each label separately
dc.format.extentp. 246-257spa
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofLecture Notes in Computer Science, 7014spa
dc.rights© Springer
dc.titleGraphical feature selection for multilabel classification tasksspa
dc.typeinfo:eu-repo/semantics/article
dc.identifier.local20111638spa
dc.identifier.doi10.1007/978-3-642-24800-9_24
dc.type.dcmitextspa
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-642-24800-9_24


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