A bias correction function for classification performance assessment in two-class imbalanced problems
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Scholar |
Otros documentos de la autoría: García, Vicente; Mollineda, Ramón A.; Sánchez Garreta, Josep Salvador
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1016/j.knosys.2014.01.021 |
Metadatos
Título
A bias correction function for classification performance assessment in two-class imbalanced problemsFecha de publicación
2014Editor
ElsevierISSN
0950-7051; 1872-7409Cita bibliográfica
GARCÍA, Vicente; MOLLINEDA, Ramón A.; SÁNCHEZ, J. Salvador. A bias correction function for classification performance assessment in two-class imbalanced problems. Knowledge-Based Systems, 2014, vol. 59, p. 66-74.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0950705114000380Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined ... [+]
This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined with the aim of highlighting those classification results that present moderately higher prediction rates on the minority class. Besides, this function punishes those scenarios that are biased towards the majority class, but also those that are strongly biased to favor the minority class. This strategy assumes a typical imbalance task, in which the minority class contains the most relevant samples to the research purposes. A novel experimental framework is designed to show the advantages of our approach when compared to the standard use of well-established measures, demonstrating its consistency and validity. [-]
Publicado en
Knowledge-Based Systems, 2014, vol. 59Derechos de acceso
Copyright © 2014 Elsevier B.V.
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