A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
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Otros documentos de la autoría: Alejo Eleuterio, Roberto; Valdovinos Rosas, Rosa María; García, Vicente; Pacheco Sánchez, J. H.
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Título
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenariosAutoría
Fecha de publicación
2013Editor
ElsevierISSN
0167-8655Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://ac.els-cdn.com/S0167865512002887/1-s2.0-S0167865512002887-main.pdf?_tid=f ...Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural ... [+]
Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method. [-]
Publicado en
Pattern Recognition Letters, 2013, Marzo, Vol. 34, nº 4Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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