Genetic algorithms with diversity measures to build classifier systems

Genetic algorithms with diversity measures to build classifier systems

The combination of classifiers is an active research area of the machine learning and pattern recognition communities. Many theoretical and empirical studies have been published demonstrating the advantages of the paradigm of combination of classifiers over the individual classifiers. When combining...

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Veröffentlicht in:Investigación operacional 2015-09, Vol.36 (3), p.206
Hauptverfasser: Cabrera Hernandez, Leidys, Morales Hernandez, Alejandro, Casas Cardoso, Gladys M, Martinez Jimenez, Yailen
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Morales Hernandez, Alejandro
Casas Cardoso, Gladys M
Martinez Jimenez, Yailen
description The combination of classifiers is an active research area of the machine learning and pattern recognition communities. Many theoretical and empirical studies have been published demonstrating the advantages of the paradigm of combination of classifiers over the individual classifiers. When combining classifiers it is important to guarantee the diversity among them [16]. Some statistical measures can be used to estimate how diverse the ensembles of classifiers are, they are called diversity measures.
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title Genetic algorithms with diversity measures to build classifier systems
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