An Investigation of Fuzzy PSO and Fuzzy SVD Based RBF Neural Network for Multi-label Classification

  • Jitendra Agrawal
  • Shikha Agrawal
  • Shilpy Kaur
  • Sanjeev Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)


Multi-label classification deals with problems where each instance is associated with multiple labels at the same time. Various techniques exist to solve the multi-label classification problem. One such technique is ML-RBF (Multi-Label Radial Basis Function), which has proved to be quite efficient. However, to further enhance the performance of the ML-RBF for multi-label classification problem, we have proposed two new algorithms. The first proposed algorithm is named as fuzzy PSO based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with SVD (Singular Value Decomposition). Both the proposed algorithms are applied to real world datasets i.e. yeast and scene dataset. The experimental results show that both the proposed algorithms meets or beats ML-RBF when applied on the test datasets.


Fuzzy particle swarm optimization Singular value decomposition Multi-label classification Multi-label radial basis function 


  1. 1.
    Zhang, M.L., Zhou, Z.H.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn. 40, 2038–3048 (2007)CrossRefMATHGoogle Scholar
  2. 2.
    Spyromitros, E., Tsoumakas, G., Vlahavas, I.: An empirical study of lazy multilabel classification algorithms. artificial intelligence: theories, models and applications. Lect. Notes Artif. Intell. 5138, 401–406 (2008)Google Scholar
  3. 3.
    Coelho, T.A., Esmin, A.A.A., Junior, W.M.: Particle swarm optimization for multi-label classification. In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, pp. 17, 18. (2011)Google Scholar
  4. 4.
    Zhang, M.L., Zhou, Z.H.: Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18, 1338–1351 (2006)CrossRefGoogle Scholar
  5. 5.
    Grodzicki, R., Mandziuk, J., Wang, L.: Improved multi-label classification with neural networks. advances in knowledge discovery and data mining. Lect. Notes Comput. Sci. 5199, 409–416 (2008)CrossRefGoogle Scholar
  6. 6.
    Zhang, M.L.: ML-RBF: RBF neural networks for multi-label learning. Neural Process Lett. 29, 61–74 (2009)CrossRefGoogle Scholar
  7. 7.
    Sapozhnikova, E.P.: Art-based neural networks for multi-label classification. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.F. (eds.): Advances in Intelligent Data Analysis VIII. Lecture Notes in Computer Science, Vol. 5772, pp. 167–177 (2009)Google Scholar
  8. 8.
    De Souza, A.F., Pedroni, F., Oliveira, E., Ciarelli, P.M., Henrique, W.F., Veronese, L., Badue, C.: Automated multi-label text categorization with VG-RAM weightless neural networks. Neurocomputing 72, 2209–2217 (2009)CrossRefGoogle Scholar
  9. 9.
    Ciarelli, P.M., Oliveria, E., Badue, C., De Souza, A.F.: Multi-label text categorization using a probabilistic neural network. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. (IJCISIM) 1, 133–144 (2009)Google Scholar
  10. 10.
    Chen, Z., Chi, Z., Fu, H., Feng, D.: Multi-instance multi-label scene classification: A neural approach. Neurocomputing 99, 298–306 (2013)CrossRefGoogle Scholar
  11. 11.
    Abdelbar, A.M., Abdelshahid, S., Wunsch II, D.C.: Fuzzy PSO: A generalization of particle swarm optimization. In: Proceedings of International Joint Conference on Neural Networks, Vol. 2, Montereal, Canada. (2005)Google Scholar
  12. 12.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C: the art of scientific computing. Cambridge University Press, New York (1992)Google Scholar
  13. 13.
    Tsoumakas, G., Katakis, I.: Multi-Label Classification: An Overview. Int. J. Data Warehouse. Min. 3, 1–13 (2007)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Jitendra Agrawal
    • 1
  • Shikha Agrawal
    • 2
  • Shilpy Kaur
    • 1
  • Sanjeev Sharma
    • 1
  1. 1.School of Information TechnologyRGPVBhopalIndia
  2. 2.University Institute of Technology, RGPVBhopalIndia

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