Classifier Ensemble Generation for the Majority Vote Rule

Authors:
Carlos Orrite;Mario Rodríguez;Francisco Martínez;Michael Fairhurst
Affiliations:
Aragon Institute for Engineering Research, University of Zaragoza, Spain;Aragon Institute for Engineering Research, University of Zaragoza, Spain;Aragon Institute for Engineering Research, University of Zaragoza, Spain;Electronics Department, University of Kent, UK
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

This paper addresses the problem of classifier ensemble generation. The goal is to obtain an ensemble to achieve maximum recognition gains with the lowest number of classifiers. The final decision is taken following a majority vote rule. If the classifiers make independent errors, the majority vote outperforms the best classifier. Therefore, the ensemble should be formed by classifiers exhibiting individual accuracy and diversity. To account for the quality of the ensemble, this work uses a sigmoid function to measure the behavior of the ensemble in relation to the majority vote rule, over a test labelled data set.