A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Títol
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenariosAutoria
Data de publicació
2013Editor
ElsevierISSN
0167-8655Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://ac.els-cdn.com/S0167865512002887/1-s2.0-S0167865512002887-main.pdf?_tid=f ...Versió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
Pattern Recognition Letters, 2013, Marzo, Vol. 34, nº 4Drets d'accés
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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