Analyzing Classifier Hierarchy Multiclassifier Learning

Authors:
J. M. Martínez-Otzeta;B. Sierra;E. Lazkano;E. Jauregi;Y. Yurramendi
Affiliations:
Fundación Tekniker, Eibar, Spain 20600;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Spain 20018;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Spain 20018;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Spain 20018;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Spain 20018
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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Abstract

Classifier combination falls in the so called machine learning area. Its aim is to combine some classification paradigms in order to improve the individual accuracy of the component classifiers. Classifier hierarchies are an alternative among the several methods of classifier combination. In this paper we present new results about a recently proposed hierarchy construction method. Experiments have been carried out over 42 databases from the UCI repository, showing an improvement over the performance of the base classifiers.