Advertisement

SVMTOCP: A Binary Tree Base SVM Approach through Optimal Multi-class Binarization

  • Diego Arab Cohen
  • Elmer Andrés Fernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

The tree architecture has been employed to solve multi-class problems based on SVM. It is an alternative to the well known OVO/OVA strategies. Most of the tree base SVM classifiers try to split the multi-class space, mostly, by some clustering like algorithms into several binary partitions. One of the main drawbacks of this is that the natural class structure is not taken into account. Also the same SVM parameterization is used for all classifiers. Here a preliminary and promising result of a multi-class space partition method that account for data base class structure and allow node‘s parameter specific solutions is presented. In each node the space is split into two class problem possibilities and the best SVM solution found. Preliminary results show that accuracy is improved, lesser information is required, each node reaches specific cost values and hard separable classes can easily be identified.

Keywords

multi-class classification SVM Binary Tree 

References

  1. 1.
    Rifkin, R., Klautau, A.: Defence of OneVs.-All Classification. Journal of Machine Learning 5, 101–141 (2004)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Platt, C.N., Shawe-Taylor, J.: Large margin DAGSVMs for multiclass classification. In: Advances in Neural Information Processing System, vol. 12, pp. 547–553 (2000)Google Scholar
  3. 3.
    Fei, B., Liu, J.: Binary Tree of SVM: A New fast Multiclass Training and Classification algorithm. IEEE Trans. on Neural Networks 17, 3 (2006)CrossRefGoogle Scholar
  4. 4.
    Madzarov, G., Gjorgjevikj, D., Chorbev, I.: A multi-class SVM Classifier Utilizing Binary Decision Tree. Informatica 33, 233–241 (2009)MathSciNetGoogle Scholar
  5. 5.
    Liu, X.P., Xing, H., Wang, X.: A multistage support vector machine. In: Proc. 2nd Conf. on Mach. Learning and Cybernetics, Xi’an (2003)Google Scholar
  6. 6.
    Ben-Hur, A., Horn, D.: Support vector Clustering. The Journal of Machine Learning Research Archive 2(3/1) (2002)Google Scholar
  7. 7.
    Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge Univ. Press (2000)Google Scholar
  8. 8.
    Gjorgji, M., Dejan, G., Ivan, C.: A Multiclass SVM Classifier Utilizing Binary Decision Tree. Informatica 33, 233–241 (2009)Google Scholar
  9. 9.
  10. 10.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Diego Arab Cohen
    • 1
  • Elmer Andrés Fernández
    • 1
    • 2
  1. 1.Biomedical Data Mining Group, Facultad de IngenieríaUniversidad Católica de CórdobaArgentina
  2. 2.CONICETArgentina

Personalised recommendations