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)


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.


Label ranking back-propagation multilayer perceptron 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aiguzhinov, A., Soares, C., Serra, A.P.: A Similarity-Based Adaptation of Naive Bayes for Label Ranking: Application to the Metalearning Problem of Algorithm Recommendation. In: Discovery Science (2010)Google Scholar
  2. 2.
    Vembu, S., Gärtner, T.: Label Ranking Algorithms: A Survey. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning. Springer (2010)Google Scholar
  3. 3.
    Hüllermeier, E., Fürnkranz, J.: On loss functions in label ranking and risk minimization by pairwise learning. JCSS 76(1), 49–62 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Kanda, J., Carvalho, A.C.P.L.F., Hruschka, E.R., Soares, C.: Using Meta-learning to Classify Traveling Salesman Problems. In: SBRN (2010)Google Scholar
  5. 5.
    Brinker, K., Hüllermeier, E.: Label Ranking in Case-Based Reasoning. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 77–91. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Dekel, O., Manning, C.D., Singer, Y.: Log-linear models for label ranking. In: Advances in Neural Information Processing Systems (2003)Google Scholar
  7. 7.
    Hülermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artif. Intell., 1897–1916 (2008)Google Scholar
  8. 8.
    Cheng, W., Dembczynski, K., Hüllermeier, E.: Label Ranking Methods based on the Plackett-Luce Model. In: ICML (2010)Google Scholar
  9. 9.
    Cheng, W., Huhn, J.C., Hüllermeier, E.: Decision tree and instance-based learning for label ranking. In: ICML (2009)Google Scholar
  10. 10.
    de Sá, C.R., Soares, C., Jorge, A.M., Azevedo, P., Costa, J.: Mining Association Rules for Label Ranking. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 432–443. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Haykin, S.: Neural Networks: a comprehensive foundation, 2nd edn (1998)Google Scholar
  12. 12.
  13. 13.
    Demǎr, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)Google Scholar
  14. 14.
    Klösgen, W.: Subgroup Discovery. In: Handbook of Data Mining and Knowledge Discovery, ch. 16.3. Oxford University Press, New York (2002)Google Scholar
  15. 15.
    Pieters, B.F.I., Knobbe, A., Džeroski, S.: Subgroup Discovery in Ranked Data, with an Application to Gene Set Enrichment. In: Proc. Preference Learning Workshop (PL 2010) at ECML PKDD (2010)Google Scholar

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

Personalised recommendations