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Improving Multi-label Classifiers via Label Reduction with Association Rules

  • Francisco Charte
  • Antonio Rivera
  • María José del Jesus
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7209)

Abstract

Multi-label classification is a generalization of well known problems, such as binary or multi-class classification, in a way that each processed instance is associated not with a class (label) but with a subset of these. In recent years different techniques have appeared which, through the transformation of the data or the adaptation of classic algorithms, aim to provide a solution to this relatively recent type of classification problem.

This paper presents a new transformation technique for multi-label classification based on the use of association rules aimed at the reduction of the label space to deal with this problem.

Keywords

Multi-label Classification Data Transformation Dimensionality Reduction Association Rules 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Charte
    • 1
  • Antonio Rivera
    • 1
  • María José del Jesus
    • 1
  • Francisco Herrera
    • 2
  1. 1.Dep. of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Dep. of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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