A Simple Approach to Incorporate Label Dependency in Multi-label Classification

  • Everton Alvares Cherman
  • Jean Metz
  • Maria Carolina Monard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)


In multi-label classification, each example can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. In this paper, we consider a simple approach which can be used to explore labels dependency aiming to accurately predict label combinations. An experimental study using decision trees, a kernel method as well as Naïve Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.


machine learning multilabel classification binary relevance label dependency 


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  1. 1.
    Qi, G.J., Hua, X.S., Rui, Y., Tang, J., Mei, T., Zhang, H.J.: Correlative multi-label video annotation. In: MULTIMEDIA 2007: Proceedings of the 15th International Conference on Multimedia, pp. 17–26. ACM, New York (2007)Google Scholar
  2. 2.
    Yang, S., Kim, S.K., Ro, Y.M.: Semantic home photo categorization. IEEE Transactions on Circuits and Systems for Video Technology 17, 324–335 (2007)CrossRefGoogle Scholar
  3. 3.
    Blockeel, H., Schietgat, L., Struyf, J., Džeroski, S., Clare, A.: Decision trees for hierarchical multilabel classification: A case study in functional genomics. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 18–29. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multilabel classification of music into emotions. In: ISMIR 2008: 9th International Conference on Music Information Retrieval, pp. 325–330 (2008)Google Scholar
  5. 5.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 1–19 (2009)Google Scholar
  6. 6.
    Prati, R.C., Batista, G.E., Monard, M.C.: Class imbalance versus class overlaping: an analysis of a learning system behaviour. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 312–321. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6, 20–29 (2004)CrossRefGoogle Scholar
  8. 8.
    Kang, F., Jin, R., Sukthankar, R.: Correlated label propagation with application to multi-label learning. In: CVPR, vol. (2), pp. 1719–1726. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  9. 9.
    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
  10. 10.
    Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Demšar, J.: Statistical comparison of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Everton Alvares Cherman
    • 1
  • Jean Metz
    • 1
  • Maria Carolina Monard
    • 1
  1. 1.Institute of Mathematics and Computer Science (ICMC)University of São Paulo at São Carlos (USP/São Carlos)São CarlosBrazil

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