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.
This work was financially supported by CONACyT (Mexico) through the project J38707-A.
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Keywords
- Classification Algorithm
- Complexity Measure
- Radial Basis Function Network
- Good Classifier
- Gaussian Radial Basis Function
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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
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