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A First Approach to Deal with Imbalance in Multi-label Datasets

  • Francisco Charte
  • Antonio Rivera
  • María José del Jesus
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

The process of learning from imbalanced datasets has been deeply studied for binary and multi-class classification. This problem also affects to multi-label datasets. Actually, the imbalance level in multi-label datasets uses to be much larger than in binary or multi-class datasets. Notwithstanding, the proposals on how to measure and deal with imbalanced datasets in multi-label classification are scarce.

In this paper, we introduce two measures aimed to obtain information about the imbalance level in multi-label datasets. Furthermore, two preprocessing methods designed to reduce the imbalance level in multi-label datasets are proposed, and their effectiveness is validated experimentally. Finally, an analysis for determining when these methods have to be applied depending on the dataset characteristics is provided.

Keywords

Multi-label Classification Imbalanced Datasets Preprocessing Measures 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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