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Multilayer Perceptron for Label Ranking

  • Geraldina Ribeiro
  • Wouter Duivesteijn
  • Carlos Soares
  • Arno Knobbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

Label Ranking problems are receiving increasing attention in machine learning. The goal is to predict not just a single value from a finite set of labels, but rather the permutation of that set that applies to a new example (e.g., the ranking of a set of financial analysts in terms of the quality of their recommendations). In this paper, we adapt a multilayer perceptron algorithm for label ranking. We focus on the adaptation of the Back-Propagation (BP) mechanism. Six approaches are proposed to estimate the error signal that is propagated by BP. The methods are discussed and empirically evaluated on a set of benchmark problems.

Keywords

Label ranking back-propagation multilayer perceptron 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geraldina Ribeiro
    • 1
  • Wouter Duivesteijn
    • 2
  • Carlos Soares
    • 3
  • Arno Knobbe
    • 2
  1. 1.Faculdade de EconomiaUniversidade do PortoPortugal
  2. 2.LIACSLeiden UniversityThe Netherlands
  3. 3.INESC TECUniversidade do PortoPortugal

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