Graphical Feature Selection for Multilabel Classification Tasks

  • Gerardo Lastra
  • Oscar Luaces
  • Jose R. Quevedo
  • Antonio Bahamonde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7014)


Multilabel 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.


Feature Selection Base Learner Machine Learn Research Binary Relevance Relevant Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gerardo Lastra
    • 1
  • Oscar Luaces
    • 1
  • Jose R. Quevedo
    • 1
  • Antonio Bahamonde
    • 1
  1. 1.Artificial Intelligence Center.University of Oviedo at GijónAsturiasSpain

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