Classifier Selection Based on Data Complexity Measures

  • Edith Hernández-Reyes
  • J. A. Carrasco-Ochoa
  • J. Fco. Martínez-Trinidad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

Tin Kam Ho and Ester Bernardò Mansilla in 2004 proposed to use data complexity measures to determine the domain of competition of the classifiers. They applied different classifiers over a set of problems of two classes and determined the best classifier for each one. Then for each classifier they analyzed how the values of some pairs of complexity measures were, and based on this analysis they determine the domain of competition of the classifiers. In this work, we propose a new method for selecting the best classifier for a given problem, based in the complexity measures. Some experiments were made with different classifiers and the results are presented.

Keywords

Classification Algorithm Complexity Measure Radial Basis Function Network Good Classifier Gaussian Radial Basis Function 
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.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Edith Hernández-Reyes
    • 1
  • J. A. Carrasco-Ochoa
    • 1
  • J. Fco. Martínez-Trinidad
    • 1
  1. 1.National Institute for Astrophysics, Optics and ElectronicsSta. Ma. TonantzintlaMéxico

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