Advertisement

A New Variant of the Optimum-Path Forest Classifier

  • João P. Papa
  • Alexandre X. Falcão
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)

Abstract

We have shown a supervised approach for pattern classification, which interprets the training samples as nodes of a complete arc-weighted graph and computes an optimum-path forest rooted at some of the closest samples between distinct classes. A new sample is classified by the label of the root which offers to it the optimum path. We propose a variant, in which the training samples are the nodes of a graph, whose the arcs are the k-nearest neighbors in the feature space. The graph is weighted on the nodes by their probability density values (pdf) and the optimum-path forest is rooted at the maxima of the pdf. The best value of k is computed by the maximum accuracy of classification in the training set. A test sample is assigned to the class of the maximum, which offers to it the optimum path. Preliminary results have shown that the proposed approach can outperform the previous one and the SVM classifier in some datasets.

Keywords

Support Vector Machine Feature Space Training Sample Optimum Path Separable Classis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Haykin, S.: Neural networks: A comprehensive foundation. Prentice-Hall, Englewood Cliffs (1994)zbMATHGoogle Scholar
  2. 2.
    Reynolds, D., Rose, R.: Robust text-independent speaker identification using gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing 3, 72–83 (1995)CrossRefGoogle Scholar
  3. 3.
    Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: 5th Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)CrossRefGoogle Scholar
  4. 4.
    Fukunaga, K., Narendra, P.M.: A branch and bound algorithms for computing k-nearest neighbors. IEEE Transactions on Computers 24, 750–753 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Papa, J., Falcão, A., Miranda, P., Suzuki, C., Mascarenhas, N.: Design of robust pattern classifiers based on pptimum-path forests. In: Mathematical Morphology and its Applications to Signal and Image Processing (ISMM), MCT/INPE, pp. 337–348 (2007)Google Scholar
  6. 6.
    Papa, J., Falcão, A., Suzuki, C., Mascarenhas, N.: A discrete approach for supervised pattern recognition. In: Brimkov, V.E., Barneva, R.P., Hauptman, H.A. (eds.) IWCIA 2008. LNCS, vol. 4958, pp. 136–147. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Rocha, L.M., Falcão, A.X., Meloni, L.G.P.: A robust extension of the mean shift algorithm using optimum path forest. In: 8th Intl. Workshop on Combinatorial Image Analysis, Buffalo-NY, USA, RPS, pp. 29–38 (2008) ISBN 978-981-08-0228-8Google Scholar
  8. 8.
    Cappabianco, F., Falcão, A., Rocha, L.: Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain. In: The Fifth IEEE Intl. Symp. on Biomedical Imaging (ISBI), Paris, France (accepted, 2008)Google Scholar
  9. 9.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience, Hoboken (2004)zbMATHCrossRefGoogle Scholar
  10. 10.
    Reyzin, L., Schapire, R.E.: How boosting the margin can also boost classifier complexity. In: 23th Intl. Conf. on Machine learning, pp. 753–760. ACM Press, New York (2006)Google Scholar
  11. 11.
    Duan, K., Keerthi, S.S.: Which is the best multiclass svm method? an empirical study. Multiple Classifier Systems, 278–285 (2005)Google Scholar
  12. 12.
    Tang, B., Mazzoni, D.: Multiclass reduced-set support vector machines. In: 23th ICML, pp. 921–928. ACM Press, New York (2006)CrossRefGoogle Scholar
  13. 13.
    Panda, N., Chang, E.Y., Wu, G.: Concept boundary detection for speeding up SVMS. In: 23th Intl. Conf. on Machine learning, pp. 681–688. ACM Press, New York (2006)Google Scholar
  14. 14.
    Collobert, R., Bengio, S.: Links between perceptrons, MLPS and SVMS. In: 21th Intl. Conf. on Machine learning, p. 23. ACM Press, New York (2004)Google Scholar
  15. 15.
    Falcão, A., Stolfi, J., Lotufo, R.: The image foresting transform: theory, algorithms, and applications. IEEE TPAMI 26, 19–29 (2004)Google Scholar
  16. 16.
    Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  17. 17.
    Montoya-Zegarra, J., Papa, J., Leite, N., Torres, R., Falcão, A.: Learning how to extract rotation-invariant and scale-invariant features from texture images. EURASIP Journal on Advances in Signal Processing 2008, 1–15 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • João P. Papa
    • 1
  • Alexandre X. Falcão
    • 1
  1. 1.Institute of ComputingState University of CampinasCampinasBrazil

Personalised recommendations

Cite paper

Buy options