Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains

  • Evgeni Tsivtsivadze
  • Tom Heskes
  • Armand Paauw
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)


In various learning problems data can be available in different representations, often referred to as views. We propose multi-class classification method that is particularly suitable for multi-view learning setting. The algorithm uses co-regularization and error-correcting techniques to leverage information from multiple views and in our empirical evaluation notably outperforms several state-of-the-art classification methods on publicly available datasets. Furthermore, we apply the proposed algorithm for identification of the pathogenic bacterial strains from the recently collected biomedical dataset. Our algorithm gives a low classification error rate of 5%, allows rapid identification of the pathogenic microorganisms, and can aid effective response to an infectious disease outbreak.


Reproduce Kernel Hilbert Space Multiple Kernel Learning Machine Learn Research Brucella Species Infectious Disease Outbreak 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Res. 2, 263–286 (1995)zbMATHGoogle Scholar
  2. 2.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Proceedings of the Neural Information Processing Systems, pp. 507–513. MIT Press, Cambridge (1998)Google Scholar
  3. 3.
    Rifkin, R., Klautau, A.: In defense of one-vs-all classification. Journal of Machine Learning Research 5, 101–141 (2004)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: Proceedings of the International Conference on Machine Learning, p. 104. ACM (2004)Google Scholar
  5. 5.
    Zien, A., Ong, C.S.: Multiclass multiple kernel learning. In: Proceedings of the International Conference on Machine Learning, pp. 1191–1198. ACM, New York (2007)Google Scholar
  6. 6.
    Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2001)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292 (2002)zbMATHGoogle Scholar
  8. 8.
    Weston, J., Schölkopf, B., Bousquet, O.: Joint kernel maps. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 176–191. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Park, S.-H., Fürnkranz, J.: Efficient decoding of ternary error-correcting output codes for multiclass classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS (LNAI), vol. 5782, pp. 189–204. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Sindhwani, V., Niyogi, P., Belkin, M.: A co-regularization approach to semi-supervised learning with multiple views. In: Proceedings of ICML Workshop on Learning with Multiple Views (2005)Google Scholar
  11. 11.
    Lista, F., Reubsaet, F., De Santis, R., Parchen, R., de Jong, A., Kieboom, J., van der Laaken, A., Voskamp-Visser, I., Fillo, S., Jansen, H.J., Van der Plas, J., Paauw, A.: Reliable identification at the species level of brucella isolates with maldi-tof-ms. BMC Microbiology 11(1), 267 (2011)CrossRefGoogle Scholar
  12. 12.
    Schölkopf, B., Herbrich, R., Smola, A.J.: A generalized representer theorem. In: Helmbold, D.P., Williamson, B. (eds.) COLT/EuroCOLT 2001. LNCS (LNAI), vol. 2111, pp. 416–426. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Rifkin, R., Yeo, G., Poggio, T.: Regularized least-squares classification. In: Advances in Learning Theory: Methods, Model and Applications, pp. 131–154. IOS Press, Amsterdam (2003)Google Scholar
  14. 14.
    Tsivtsivadze, E., Pahikkala, T., Boberg, J., Salakoski, T., Heskes, T.: Co-regularized least-squares for label ranking. In: Hüllermeier, E., Fürnkranz, J. (eds.) Preference Learning, pp. 107–123 (2010)Google Scholar
  15. 15.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM, New York (1998)CrossRefGoogle Scholar
  16. 16.
    Brefeld, U., Gärtner, T., Scheffer, T., Wrobel, S.: Efficient co-regularised least squares regression. In: Proceedings of the International Conference on Machine Learning, pp. 137–144. ACM, New York (2006)Google Scholar
  17. 17.
    Brefeld, U., Scheffer, T.: Auc maximizing support vector learning. In: Proceedings of ICML Workshop on ROC Analysis in Machine Learning (2005)Google Scholar
  18. 18.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)zbMATHGoogle Scholar
  19. 19.
    Liu, Q., Sung, A.H., Qiao, M., Chen, Z., Yang, J.Y., Yang, M.Q.Q., Huang, X., Deng, Y.: Comparison of feature selection and classification for MALDI-MS data. BMC Genomics 10(suppl. 1) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Evgeni Tsivtsivadze
    • 1
  • Tom Heskes
    • 2
  • Armand Paauw
    • 1
  1. 1.MSB GroupThe Netherlands Organization for Applied Scientific ResearchZeistThe Netherlands
  2. 2.Institute for Computing and Information SciencesRadboud UniversityThe Netherlands

Personalised recommendations