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. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Hernández-Reyes, E., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2005). Classifier selection based on data complexity measures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3773 LNCS, pp. 586–592). Springer Verlag. https://doi.org/10.1007/11578079_61
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