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Combining One-Versus-One and One-Versus-All Strategies to Improve Multiclass SVM Classifier

  • Wiesław ChmielnickiEmail author
  • Katarzyna Sta̧por
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

Support Vector Machine (SVM) is a binary classifier, but most of the problems we find in the real-life applications are multiclass. There are many methods of decomposition such a task into the set of smaller classification problems involving two classes only. Two of the widely known are one-versus-one and one-versus-rest strategies. There are several papers dealing with these methods, improving and comparing them. In this paper, we try to combine theses strategies to exploit their strong aspects to achieve better performance. As the performance we understand both recognition ratio and the speed of the proposed algorithm. We used SVM classifier on several different databases to test our solution. The results show that we obtain better recognition ratio on all tested databases. Moreover, the proposed method turns out to be much more efficient than the original one-versus-one strategy.

References

  1. 1.
    Allwein, E., Schapire, R., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2001)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm (2001)
  3. 3.
    Chen, D.R., Wu, Q., Ying, Y., Zhou, D.X.: Support vector machine soft margin classifiers: error analysis. J. Mach. Learn. Res. 5, 1143–1175 (2004)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Chmielnicki, W., Stapor, K.: Protein fold recognition with combined SVM-RDA classifier. Lect. Notes Artif. Intell. 6076, 162–169 (2010)Google Scholar
  5. 5.
    Chmielnicki, W., Stapor, K.: A hybrid discriminative/generative approach to protein fold recognition. Neurocomputing 75(1), 194–198 (2012)CrossRefGoogle Scholar
  6. 6.
    Chmielnicki, W., Stapor, K.: A modification of the pairwise coupling algorithm to solve multi-class problem. In: Proceedings of XLIII Conference on Mathematics Applications, in Polish (2014)Google Scholar
  7. 7.
    Chmielnicki, W., Roterman-Konieczna, I., Stapor, K.: An improved protein fold recognition with support vector machines. Expert Syst. 20(2), 200–211 (2012)Google Scholar
  8. 8.
    Ding, C.H., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001)CrossRefGoogle Scholar
  9. 9.
    Dietterich, T.G., Bakiri, G.: Solving multiclass problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)zbMATHGoogle Scholar
  10. 10.
    Dubchak, I., Muchnik, I., Holbrook, S.R., Kim, S.H.: Prediction of protein folding class using global description of amino acid sequence. Proc. Natl. Acad. Sci. USA 92, 8700–8704 (1995)CrossRefGoogle Scholar
  11. 11.
    Fei, B., Liu, J.: Binary tree of SVM: a new fast multiclass training and classification algorithm. IEEE Trans. Neural Netw. 17(3), 696–704 (2006)CrossRefGoogle Scholar
  12. 12.
    Friedman, J.H.: Another approach to polychotomous classification. Stanford Department of Statistics (1996)Google Scholar
  13. 13.
    Galar, M., Fernandez, A., Tartas, E.B., Sola, B., Herrera, F.: Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers. Pattern Recognit. 46(12), 3412–3424 (2013)CrossRefGoogle Scholar
  14. 14.
    Glomb, P., Romaszewski, M., Opozda, S., Sochan, A.: Choosing and modeling hand gesture database for natural user interface. In: Proceedings of the 9th International Conference on Gesture and Sign Language in Human-Computer Interaction and Embodied Communication, pp. 24–35 (2011)Google Scholar
  15. 15.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Ann. Stat. 26(2), 451–471 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
  17. 17.
    Hobohm, U., Scharf, M., Schneider, R., Sander, C.: Selection of a representative set of structures from the Brookhaven protein bank. Protein Sci. 1, 409–417 (1992)CrossRefGoogle Scholar
  18. 18.
    Kijsirikul, B., Ussivakul, N.: Multiclass support vector machines using adaptive directed acyclic graph. In: Proceedings of the International Joint Conference on Neural Networks, pp. 980–985 (2002)Google Scholar
  19. 19.
    Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30(1), 25–36 (2006)Google Scholar
  20. 20.
    Krawczyk, B., Wozniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Inf. Sci. 264, 182–195 (2014)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Lorena, A.C., Carvalho, A.C., Gama, J.M.: A review on the combination of binary classifiers in multiclass problems. Artif. Intell. Rev. 30(1–4), 19–37 (2008)CrossRefGoogle Scholar
  22. 22.
    Lorena, A.C., Carvalho, A.C.: Building binary-tree-based multiclass classifiers using separability measures. Neurocomputing 73(16–18), 2837–2845 (2010)CrossRefGoogle Scholar
  23. 23.
    Moreira, M., Mayoraz, E.: Improved pairwise coupling classification with correcting classifiers. In: Proceedings of Tenth European Conference on Machine Learning ECML, Chemmitz, Germany, pp. 160–171 (1998)Google Scholar
  24. 24.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Proceedings of Neural Information Processing Systems, pp. 547–553 (2000)Google Scholar
  25. 25.
    Sáez, J.A., Galar, M., Luengo, J., Herrera, F.: A first study on decomposition strategies with data with class noise using decision trees. In: Proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems—Volume Part II, pp. 25–35 (2012)Google Scholar
  26. 26.
    Silva, P.F.B., Marcal, A.R.S., Almeida da Silva, R.M.: Evaluation of Features for Leaf Discrimination. Springer Lecture Notes in Computer Science, vol. 7950, pp. 197–204. Springer, Heidelberg (2013)Google Scholar
  27. 27.
    UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/ (2014)
  28. 28.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  29. 29.
    Vural, V., Dy, J.G.: A hierarchical method for multi-class support vector machines. In: Proceedings of the XXI ICML, pp. 831–838 (2004)Google Scholar
  30. 30.
    Windeatt, T., Ghaderi, R.: Coding and decoding for multiclass learning problems. Inf. Fusion 4(1), 11–21 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Physics, Astronomy and Applied Computer ScienceJagiellonian UniversityKrakowPoland
  2. 2.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

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