ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning

  • Oscar Gabriel Reyes Pupo
  • Carlos Morell
  • Sebastián Ventura Soto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

In the last years, the learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of modern applications that contain this type of data. Several methods have been proposed for solving this problem, however how to make feature weighting on multi-label data is still lacking in the literature. In multi-label data, each data point can be attributed to multiple labels simultaneously, thus a major difficulty lies in the determinations of the features useful for all multi-label concepts. In this paper, a new method for feature weighting in multi-label learning area is presented, based on the principles of the well-known ReliefF algorithm. The experimental stage shows the effectiveness of the proposal.

Keywords

multi-label learning feature weighting ReliefF algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Oscar Gabriel Reyes Pupo
    • 1
  • Carlos Morell
    • 2
  • Sebastián Ventura Soto
    • 3
  1. 1.University of HolguínCuba
  2. 2.Universidad Central ”Marta Abreu” de Las VillasCuba
  3. 3.University of CórdobaSpain

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