A new framework for optimal classifier design

Matías Di Martino, Guzmán Hernández, Marcelo Fiori, Alicia Fernández

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

24 Citas (Scopus)

Resumen

The use of alternative measures to evaluate classifier performance is gaining attention, specially for imbalanced problems. However, the use of these measures in the classifier design process is still unsolved. In this work we propose a classifier designed specifically to optimize one of these alternative measures, namely, the so-called F-measure. Nevertheless, the technique is general, and it can be used to optimize other evaluation measures. An algorithm to train the novel classifier is proposed, and the numerical scheme is tested with several databases, showing the optimality and robustness of the presented classifier.

Idioma originalInglés
Páginas (desde-hasta)2249-2255
Número de páginas7
PublicaciónPattern Recognition
Volumen46
N.º8
DOI
EstadoPublicada - ago. 2013
Publicado de forma externa

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