Multi-label Testing for CO2RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification

  • A. J. Rivera
  • F. Charte
  • M. D. Pérez-Godoy
  • María Jose del Jesus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6691)

Abstract

While in traditional classification an instance of the data set is only associated with one class, in multi-label classification this instance can be associated with more than one class or label. Examples of applications in this growing area are text categorization, functional genomics and association of semantic information to audio or video content. One way to address these applications is the Problem Transformation methodology that transforms the multi-label problem into one single-label classification problem, in order to apply traditional classification methods. The aim of this contribution is to test the performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs, in a multi-label environment, using the problem transformation methodology. The results obtained by CO2RBFN, and by other classical data mining methods, show that no algorithm outperforms the other on all the data.

Keywords

Multi-label Classification RBFNs Problem Transformation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Broomhead, D., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355 (1988)MathSciNetMATHGoogle Scholar
  2. 2.
    Buchtala, O., Klimek, M., Sick, B.: Evolutionary optimization of radial basis function classifiers for data mining applications. IEEE Transactions on System, Man and Cybernetics B 35(5), 928–947 (2005)CrossRefGoogle Scholar
  3. 3.
    Carvalho, A.C.P.L.F., Freitas, A.A.: Foundations of Computational Intelligence. In: Abraham, A., Hassanien, A.-E., Snášel, V. (eds.) Foundations of Computational Intelligence Volume 5. SCI, vol. 205, pp. 177–195. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Clare, A., King, R.: Knowledge discovery in multi-label phenotype data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Advances in Neural Information Processing Systems 14 (2002)Google Scholar
  6. 6.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  7. 7.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11(1) (2009)Google Scholar
  8. 8.
    Harpham, C., Dawson, C.W., Brown, M.R.: A review of genetic algorithms applied to training radial basis function networks. Neural Computing and Applications 13, 193–201 (2004)CrossRefGoogle Scholar
  9. 9.
    Mandani, E., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)CrossRefMATHGoogle Scholar
  10. 10.
    Pérez-Godoy, M.D., Rivera, A.J., del Jesus, M.J., Berlanga, F.J.: CO 2 RBFN: An evolutionary cooperative-competitive RBFN design algorithm for classification problems. Soft Computing 14(9), 953–971 (2010)CrossRefGoogle Scholar
  11. 11.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Data Mining and Knowledge Discovery Handbook, 2nd edn., pp. 667–668. Springer, Heidelberg (2010)Google Scholar
  12. 12.
    Whitehead, B., Choate, T.: Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Transactions on Neural Networks 7(4), 869–880 (1996)CrossRefGoogle Scholar
  13. 13.
    Widrow, B., Lehr, M.A.: 30 years of adaptive neural networks: perceptron, madaline and backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)CrossRefGoogle Scholar
  14. 14.
    Zhang, M.L.: Ml-rbf: Rbf neural networks for multi-label learning. Neural Processing Letters 29(2), 61–74 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition 40, 2038–2048 (2007)CrossRefMATHGoogle Scholar
  16. 16.
    Zhang, Y., Burer, S., Street, W.N.: Ensemble pruning via semi-definite programming. Journal of Machine Learning Research 7, 1315–1338 (2006)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. J. Rivera
    • 1
  • F. Charte
    • 1
  • M. D. Pérez-Godoy
    • 1
  • María Jose del Jesus
    • 1
  1. 1.Dep. of Computer ScienceUniversity of JaénJaénSpain

Personalised recommendations