Improving Multi-label Classifiers via Label Reduction with Association Rules

  • Francisco Charte
  • Antonio Rivera
  • María José del Jesus
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7209)


Multi-label classification is a generalization of well known problems, such as binary or multi-class classification, in a way that each processed instance is associated not with a class (label) but with a subset of these. In recent years different techniques have appeared which, through the transformation of the data or the adaptation of classic algorithms, aim to provide a solution to this relatively recent type of classification problem.

This paper presents a new transformation technique for multi-label classification based on the use of association rules aimed at the reduction of the label space to deal with this problem.


Multi-label Classification Data Transformation Dimensionality Reduction Association Rules 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chan, A., Freitas, A.A.: A new ant colony algorithm for multi-label classification with applications in bioinfomatics. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 27–34 (2006)Google Scholar
  2. 2.
    de Carvalho, A., Freitas, A.: A Tutorial on Multi-label Classification Techniques. Foundations of Computational Intelligence 5, 177–195 (2009)Google Scholar
  3. 3.
    Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Neural Information Processing Systems, 681–687 (2001)Google Scholar
  4. 4.
    Clare, A.J., King, R.D.: Knowledge Discovery in Multi-Label Phenotype Data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, p. 42. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Karalic, A., Pirnat, V.: Significance level based multiple tree classification. Informatica 15(5) (1991)Google Scholar
  6. 6.
    Zhu, B., Poon, C.K.: Efficient Approximation Algorithms for Multi-label Map Labeling. In: Proceedings of the 10th International Symposium on Algorithms and Computation, pp. 143–152 (1999)Google Scholar
  7. 7.
    Comité, F., Gilleron, R., Tommasi, M.: Learning Multi-label Alternating Decision Trees from Texts and Data. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 35–49. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685 (2010)Google Scholar
  9. 9.
    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
  10. 10.
    Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: MULAN: A Java Library for Multi-Label Learning. Journal of Machine Learning Research, 2411–2414 (2011)Google Scholar
  11. 11.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of the 2000 ACM-SIGMOD International Conference on Management of Data, vol. 29(2), pp. 1–12 (2000)Google Scholar
  12. 12.
    Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining - a general survey and comparison. ACM SIGKDD Explorations Newsletter 2(1), 58–64 (2000)CrossRefGoogle Scholar
  13. 13.
    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 Part I, vol. 5782, pp. 254–269. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37, 1757–1771 (2004)CrossRefGoogle Scholar
  15. 15.
    Zhang, M., Zhou, Z.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition 40, 2038–2048 (2007)zbMATHCrossRefGoogle Scholar
  16. 16.
    Zhang, M., Zhou, Z.: Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization. IEEE Transactions on Knowledge and Data Engineering 18, 1338–1351 (2006)CrossRefGoogle Scholar
  17. 17.
    Zhang, M., Zhou, Z.: A k-nearest neighbor based algorithm for multi-label. In: Proceedings of the 1st IEEE International Conference on Granular Computing, pp. 718–721 (2005)Google Scholar
  18. 18.
    Zhang, M.: Ml-rbf: RBF Neural Networks for Multi-Label Learning. Neural Processing Letters 29, 61–74 (2009)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Cheng, W., Hüllermeier, E.: Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. Machine Learning 76, 211–225 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Charte
    • 1
  • Antonio Rivera
    • 1
  • María José del Jesus
    • 1
  • Francisco Herrera
    • 2
  1. 1.Dep. of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Dep. of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Personalised recommendations