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.
This work was financially supported by CONACyT (Mexico) through the project J38707-A.
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References
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© 2005 Springer-Verlag Berlin Heidelberg
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Hernández-Reyes, E., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2005). Classifier Selection Based on Data Complexity Measures. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_61
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DOI: https://doi.org/10.1007/11578079_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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