MLeNN: A First Approach to Heuristic Multilabel Undersampling

  • Francisco Charte
  • Antonio J. Rivera
  • María J. del Jesus
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8669)


Learning from imbalanced multilabel data is a challenging task that has attracted considerable attention lately. Some resampling algorithms used in traditional classification, such as random undersampling and random oversampling, have been already adapted in order to work with multilabel datasets.

In this paper MLeNN (MultiLabel edited Nearest Neighbor), a heuristic multilabel undersampling algorithm based on the well-known Wilson’s Edited Nearest Neighbor Rule, is proposed. The samples to be removed are heuristically selected, instead of randomly picked. The ability of MLeNN to improve classification results is experimentally tested, and its performance against multilabel random undersampling is analyzed. As will be shown, MLeNN is a competitive multilabel undersampling alternative, able to enhance significantly classification results.


Multilabel Classification Imbalanced Learning Resampling ENN 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang, M.-L., Zhou, Z.-H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. (2013)Google Scholar
  2. 2.
    He, J., Gu, H., Liu, W.: Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites. PloS One 7(6), 7155 (2012)Google Scholar
  3. 3.
    Charte, F., Rivera, A., del Jesus, M.J., Herrera, F.: A first approach to deal with imbalance in multi-label datasets. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS, vol. 8073, pp. 150–160. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. on SMC-2(3), 408–421 (1972)Google Scholar
  5. 5.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, ch. 34, pp. 667–685. Springer US, Boston (2010)Google Scholar
  6. 6.
    Haibo, H., Yunqian, M.: Imbalanced Learning: Foundations, Algorithms, and Applications. Wiley-IEEE Press (2013)Google Scholar
  7. 7.
    Tahir, M.A., Kittler, J., Bouridane, A.: Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recognit. Lett. 33(5), 513–523 (2012)CrossRefGoogle Scholar
  8. 8.
    García, V., Sánchez, J., Mollineda, R.: On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl. Based Systems 25(1), 13–21 (2012)CrossRefGoogle Scholar
  9. 9.
    Tsoumakas, G., Xioufis, E.S., Vilcek, J., Vlahavas, I.: MULAN: A Java Library for Multi-Label Learning. J. Mach. Learn. Res. 12, 2411–2414 (2011)MathSciNetzbMATHGoogle 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.
    Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73, 133–153 (2008)CrossRefGoogle Scholar
  12. 12.
    Tsoumakas, G., Vlahavas, I.: 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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

Personalised recommendations