Aggregating Independent and Dependent Models to Learn Multi-label Classifiers

  • Elena Montañés
  • José Ramón Quevedo
  • Juan José del Coz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)


The aim of multi-label classification is to automatically obtain models able to tag objects with the labels that better describe them. Despite it could seem like any other classification task, it is widely known that exploiting the presence of certain correlations between labels helps to improve the classification performance. In other words, object descriptions are usually not enough to induce good models, also label information must be taken into account. This paper presents an aggregated approach that combines two groups of classifiers, one assuming independence between labels, and the other considering fully conditional dependence among them. The framework proposed here can be applied not only for multi-label classification, but also in multi-label ranking tasks. Experiments carried out over several datasets endorse the superiority of our approach with regard to other methods in terms of some evaluation measures, keeping competitiveness in terms of others.


Dependent Model Jaccard Index Binary Relevance Binary Output Relevant Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76(2-3), 211–225 (2009)CrossRefGoogle Scholar
  2. 2.
    Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: European Conf. on Data Mining and Knowledge Discovery, pp. 42–53 (2001)Google Scholar
  3. 3.
    Dembczynski, K., Cheng, W., Hüllermeier, E.: Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains. In: ICML, pp. 279–286 (2010)Google Scholar
  4. 4.
    Dembczyński, K., Waegeman, W., Cheng, W., Hüllermeier, E.: Regret analysis for performance metrics in multi-label classification: The case of hamming and subset zero-one loss. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS, vol. 6321, pp. 280–295. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Elisseeff, A., Weston, J.: A Kernel Method for Multi-Labelled Classification. In: ACM Conf. on Research and Develop. in Infor. Retrieval, pp. 274–281 (2005)Google Scholar
  7. 7.
    Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking. Machine Learning 73, 133–153 (2008)CrossRefGoogle Scholar
  8. 8.
    García, S., Herrera, F.: An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. Journal of Machine Learning Research 9, 2677–2694 (2008)zbMATHGoogle Scholar
  9. 9.
    Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: ACM Int. Conf. on Information and Knowledge Management, pp. 195–200. ACM, New York (2005)Google Scholar
  10. 10.
    Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Pacific-Asia Conf. on Know. Disc. and Data Mining, pp. 22–30 (2004)Google Scholar
  11. 11.
    Lin, C.-J., Weng, R.C., Keerthi, S.S.: Trust region Newton method for logistic regression. Journal of Machine Learning Research 9(apr), 627–650 (2008)MathSciNetzbMATHGoogle Scholar
  12. 12.
    McCallum, A.K.: Multi-label text classification with a mixture model trained by em. In: AAAI 1999 Workshop on Text Learning (1999)Google Scholar
  13. 13.
    Qi, G.J., Hua, X.S., Rui, Y., Tang, J., Mei, T., Zhang, H.J.: Correlative multi-label video annotation. In: Proceedings of the International conference on Multimedia, pp. 17–26. ACM, New York (2007)Google Scholar
  14. 14.
    Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: IEEE Int. Conf. on Data Mining, pp. 995–1000. IEEE, Los Alamitos (2008)Google Scholar
  15. 15.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 254–269. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Schapire, R.E., Singer, Y.: Boostexter: A boosting-based system for text categorization. Machine Learning, 135–168 (2000)Google Scholar
  17. 17.
    Tsoumakas, G., Dimou, A., Spyromitros, E., Mezaris, V., Kompatsiaris, I., Vlahavas, I.: Correlation-based pruning of stacked binary relevance models for multi-label learning. In: Workshop on Learning from Multi-Label Data, Bled, Slovenia, pp. 101–116 (2009)Google Scholar
  18. 18.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685 (2010)Google Scholar
  19. 19.
    Tsoumakas, G., Vlahavas, I.P.: Random k-Labelsets: An Ensemble Method for Multilabel Classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Wolpert, D.H.: Stacked generalization. Neural Networks 5, 214–259 (1992)CrossRefGoogle Scholar
  21. 21.
    Zhang, M.-L., Zhou, Z.-H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. on Knowl. and Data Eng. 18, 1338–1351 (2006)CrossRefGoogle Scholar
  22. 22.
    Zhang, M.-L., Zhou, Z.-H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–2048 (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elena Montañés
    • 1
  • José Ramón Quevedo
    • 1
  • Juan José del Coz
    • 1
  1. 1.Artificial Intelligence CenterUniversity of OviedoGijónSpain

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