This work focuses on label ranking, a particular task of preference learning, wherein the problem is to learn a mapping from instances to rankings over a finite set of labels. This paper discusses and proposes alternative reduction techniques that decompose the original problem into binary classification related to pairs of labels and that can take into account label correlation during the learning process.


Preference Learning Label Ranking Reduction Techniques Machine Learning Binary Classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fürnkranz, J., Hüllermeier, E. (eds.): Preference Learning. Springer (2010)Google Scholar
  2. 2.
    Gurrieri, M., Siebert, X., Fortemps, P., Greco, S., Słowiński, R.: Label Ranking: A New Rule-Based Label Ranking Method. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012, Part I. CCIS, vol. 297, pp. 613–623. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Gurrieri, M., Siebert, X., Fortemps, P., Slowinski, R., Greco, S.: Reduction from Label Ranking to Binary Classification. In: DA2PL 2012 From Multiple Criteria Decision Aid to Preference Learning, pp. 3–13. UMONS (Université de Mons), Mons (2012)Google Scholar
  4. 4.
    Har-Peled, S., Roth, D., Zimak, D.: Constraint classification for multiclass classificatin and ranking. In: Advances in Neural Information Processing Systems, pp. 785–792 (2002)Google Scholar
  5. 5.
    Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label Ranking by learning pairwise preference. Artif. Intell. 172(16-17), 1897–1916 (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    Cheng, W., Hühn, J., Hüllermeier, E.: Decision Tree and Instance-Based Learning for Labele Ranking. In: Proc. ICML 2009, International Conference on Machine Learning, Montreal, Canada (2009)Google Scholar
  7. 7.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Machine Learning 85(3), 333–359 (2011)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Gärtner, T., Vembu, S.: Label Ranking Algorithms: A Survey. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning. Springer (2010)Google Scholar
  9. 9.
    Dekel, O., Manning, C.D., Singer, Y.: Log-linear models for label ranking. In: Advances in Neural Information Processing Systems, vol. 16 (2003)Google Scholar
  10. 10.
    Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, vol. 14 (2001)Google Scholar
  11. 11.
    Bouyssou, D.: Ranking methods based on valued preference relations: A characterization of the net flow method. European Journal of Operational Research 60(1), 61–67 (1992)CrossRefzbMATHGoogle Scholar
  12. 12.
    Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 1–30 (2006)zbMATHGoogle Scholar
  13. 13.
    Cheng, W., Hüllermeier, E., Dembczynski, K.J.: Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 279–286 (2010)Google Scholar
  14. 14.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation revisited (2001)Google Scholar
  15. 15.
    Schalekamp, F., van Zuylen, A.: Rank Aggregation: Together We’re Strong. In: ALENEX, pp. 38–51 (2009)Google Scholar
  16. 16.
    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
  17. 17.
    Diaconis, P., Graham, R.L.: Spearman’s footrule as a measure of disarray. Journal of the Royal Statistical Society, Series B (Methodological) 39, 262–268 (1977)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Hung, M.S., Hu, M.Y., Shanker, M.S., Patuwo, B.E.: Estimating posterior probabilities in classification problems with neural networks. International Journal of Computational Intelligence and Organizations 1(1), 49–60 (1996)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Massimo Gurrieri
    • 1
  • Philippe Fortemps
    • 1
  • Xavier Siebert
    • 1
  1. 1.UMonsMonsBelgium

Personalised recommendations