Selecting Objects for ALVOT

  • Miguel Angel Medina-Pérez
  • Milton García-Borroto
  • Yenny Villuendas-Rey
  • José Ruiz-Shulcloper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


ALVOT is a model of supervised classification based on partial precedences. In this paper a new object selection method based on a voting procedure for ALVOT is proposed. The method was developed for dealing with databases having objects described by features that are not exclusively numeric or categorical. A comparative numerical experiment was performed with different algorithms of object selection. The experimental results show a good performance of the proposed method with respect to the other algorithms.


Radial Base Function Network Supervise Classification Partial Evaluation Vote Power Partial Precedence 
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.


  1. 1.
    Bezdek, J.C., Kuncheva, L.I.: Nearest Prototype classifiers designs: an experimental study. International Journal of Intelligent Systems 16, 1445–1473 (2001)MATHCrossRefGoogle Scholar
  2. 2.
    Martínez Trinidad, J.F., Guzmán-Arenas, A.: The logical combinatorial approach to Pattern Recognition, an overview through selected works. Pattern Recognition 34, 741–751 (2001)MATHCrossRefGoogle Scholar
  3. 3.
    Zhuravlev, Y.I., Nikiforov, V.V.: Recognition algorithms based on voting calculation. Journal Kibernetika 3, 1–11 (1971)Google Scholar
  4. 4.
    Ruiz-Shulcloper, J., Abidi, M.A.: Logical Combinatorial Pattern Recognition: A Review. In: Pandalai, S.G. (ed.) Recent Research Developments in Pattern Recognition, USA, pp. 133–176 (2002)Google Scholar
  5. 5.
    Gómez-Herrera, J., Rodríguez-Morán, O., Valladares-Amaro, S., Ruiz-Shulcloper, J., Pico-Peña, R.: Prognostic of Gas-oil deposits in the Cuban ophiological association, applying mathematical modeling. Geofísica Internacional 33, 447–467 (1995)Google Scholar
  6. 6.
    Ortiz-Posadas, M.R.: Prognosis and evaluation of cleft palate patients’ rehabilitation using pattern recognition techniques. World Congress on Medical Physics and Biomedical Engineering 35, 500 (1997)Google Scholar
  7. 7.
    Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: Editing and Training for ALVOT, an Evolutionary Approach. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 452–456. Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: Combining Evolutionary Techniques to Improve ALVOT Efficiency. WSEAS Transactions on Systems 2, 1073–1078 (2003)Google Scholar
  9. 9.
    Kuncheva, L.I., Bezdek, J.C.: Nearest prototype classification: clustering, genetic algorithms or random search. IEEE Transactions on Systems, Man and Cybernetics. Part C 28, 160–164 (1998)CrossRefGoogle Scholar
  10. 10.
    Decaestecker, C.: NNP: A neural net classifier using prototype. In: International Conference on Neural Networks, San Francisco, California, pp. 822–824 (1993)Google Scholar
  11. 11.
    König, A., Rashhofer, R.J., Glesner, M.: A novel method for the design of radial-basis-function networks and its implication for knowledge extraction. In: International Conference on Neural Networks, Orlando, Florida, pp. 1804–1809 (1994)Google Scholar
  12. 12.
    García-Borroto, M., Ruiz-Shulcloper, J.: Selecting Prototypes in Mixed Incomplete Data. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 450–459. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics 2, 408–421 (1972)MATHCrossRefGoogle Scholar
  14. 14.
    Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. University of California at Irvine, Department of Information and Computer Science, Irvine (1998)Google Scholar
  15. 15.
    Lazo-Cortés, M., Ruiz-Shulcloper, J.: Determining the feature informational weight for non-classical described objects and new algorithm to calculate fuzzy testors. Pattern Recognition Letters 16, 1259–1265 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miguel Angel Medina-Pérez
    • 1
  • Milton García-Borroto
    • 2
  • Yenny Villuendas-Rey
    • 1
  • José Ruiz-Shulcloper
    • 3
  1. 1.University of Ciego de ÁvilaCuba
  2. 2.Bioplants CenterUNICA, C. de ÁvilaCuba
  3. 3.Advanced Technologies Applications CenterMINBASCuba

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