Classifier selection based on data complexity measures

Authors:
Edith Hernández-Reyes;J. A. Carrasco-Ochoa;J. Fco. Martínez-Trinidad
Affiliations:
National Institute for Astrophysics, Optics and Electronics, Sta. Ma. Tonantzintla, Puebla, México;National Institute for Astrophysics, Optics and Electronics, Sta. Ma. Tonantzintla, Puebla, México;National Institute for Astrophysics, Optics and Electronics, Sta. Ma. Tonantzintla, Puebla, México
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

Citing 4
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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.