Advertisement

Multi-labeler Classification Using Kernel Representations and Mixture of Classifiers

  • D. E. Imbajoa-Ruiz
  • I. D. Gustin
  • M. Bolaños-Ledezma
  • A. F. Arciniegas-Mejía
  • F. A. Guasmayan-Guasmayan
  • M. J. Bravo-Montenegro
  • A. E. Castro-Ospina
  • D. H. Peluffo-Ordóñez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)

Abstract

This work introduces a multi-labeler kernel novel approach for data classification learning from multiple labelers. The learning process is done by training support-vector machine classifiers using the set of labelers (one labeler per classifier). The objective functions representing the boundary decision of each classifier are mixed by means of a linear combination. Followed from a variable relevance, the weighting factors are calculated regarding kernel matrices representing each labeler. To do so, a so-called supervised kernel function is also introduced, which is used to construct kernel matrices. Our multi-labeler method reaches very good results being a suitable alternative to conventional approaches.

Keywords

Multi-labeler classification Supervised kernel Support vector machines 

References

  1. 1.
    Yan, Y., Fung, G.M., Rosales, R., Dy, J.G.: Active learning from crowds. In: Proceedings of 28th International Conference on Machine Learning (ICML-2011), pp. 1161–1168 (2011)Google Scholar
  2. 2.
    Dekel, O., Gentile, C., Sridharan, K.: Selective sampling and active learning from single and multiple teachers. J. Mach. Learn. Res. 13(1), 2655–2697 (2012)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Dekel, O., Shamir, O.: Good learners for evil teachers. In: ICML, vol. 30 (2009)Google Scholar
  4. 4.
    Donmez, P., Carbonell, J.G., Schneider, J.: Efficiently learning the accuracy of labeling sources for selective sampling. In: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259–268. ACM (2009)Google Scholar
  5. 5.
    Wang, W., Zhou, Z.: Learnability of multi-instance multi-label learning. Chin. Sci. Bull. 57(19), 2488–2491 (2012)CrossRefGoogle Scholar
  6. 6.
    Murillo, S., Peluffo, D.H., Castellanos, G.: Support vector machine-based approach for multi-labelers problems. In: European Symposium on Artificial Neural Networks, Computational Inteligence and Machine Learning (2013)Google Scholar
  7. 7.
    Zhang, Y., Yeung, D.Y.: Multilabel relationship learning. ACM Trans. Knowl. Discov. Data (TKDD) 7(2), 7 (2013)Google Scholar
  8. 8.
    Cerri, R., de Carvalho, A.C.P., Freitas, A.A.: Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification. Intell. Data Anal. 15(6), 861–887 (2011)Google Scholar
  9. 9.
    Murillo-Rendón, S., Peluffo-Ordóñez, D., Arias-Londoño, J.D., Castellanos-Domínguez, C.G.: Multi-labeler analysis for bi-class problems based on soft-margin support vector machines. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., Paz López, F., Toledo Moreo, F.J. (eds.) IWINAC 2013. LNCS, vol. 7930, pp. 274–282. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38637-4_28 CrossRefGoogle Scholar
  10. 10.
    Peluffo-Ordóñez, D.H., Rendón, S.M., Arias-Londoño, J.D., Castellanos-Domínguez, G.: A multi-class extension for multi-labeler support vector machines. In: European Symposium on Artificial Neural Networks (ESANN) (2014)Google Scholar
  11. 11.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)CrossRefGoogle Scholar
  12. 12.
    Peluffo-Ordonez, D.H., Aldo Lee, J., Verleysen, M.: Generalized kernel framework for unsupervised spectral methods of dimensionality reduction. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 171–177. IEEE (2014)Google Scholar
  13. 13.
    Pant, R., Trafalis, T.B.: SVM classification of uncertain data using robust multi-kernel methods. In: Migdalas, A., Karakitsiou, A. (eds.) Optimization, Control, and Applications in the Information Age. PROMS, vol. 130. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-18567-5_13 Google Scholar
  14. 14.
    Peluffo, D.H., Lee, J.A., Verleysen, M., Rodríguez-Sotelo, J.L., Castellanos-Domínguez, G.: Unsupervised relevance analysis for feature extraction and selection: a distance-based approach for feature relevance. In: International Conference on Pattern Recognition, Applications and Methods - ICPRAM 2014 (2014)Google Scholar
  15. 15.
    Peluffo-Ordóñez, D.H., Castro-Ospina, A.E., Alvarado-Pérez, J.C., Revelo-Fuelagán, E.J.: Multiple kernel learning for spectral dimensionality reduction. In: Pardo, A., Kittler, J. (eds.) CIARP 2015. LNCS, vol. 9423, pp. 626–634. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25751-8_75 CrossRefGoogle Scholar
  16. 16.
    Lichman, M.: UCI machine learning repository (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • D. E. Imbajoa-Ruiz
    • 1
  • I. D. Gustin
    • 1
  • M. Bolaños-Ledezma
    • 1
  • A. F. Arciniegas-Mejía
    • 1
  • F. A. Guasmayan-Guasmayan
    • 1
    • 2
  • M. J. Bravo-Montenegro
    • 2
  • A. E. Castro-Ospina
    • 3
  • D. H. Peluffo-Ordóñez
    • 1
    • 4
  1. 1.Universidad de NariñoPastoColombia
  2. 2.Universidad MarianaPastoColombia
  3. 3.Research Center of the Instituto Tecnológico MetropolitanoMedellínColombia
  4. 4.Universidad Técnica del NorteIbarraEcuador

Personalised recommendations