A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
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Other documents of the author: Alejo Eleuterio, Roberto; Valdovinos Rosas, Rosa María; García, Vicente; Pacheco Sánchez, J. H.
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenariosAuthor (s)
Date
2013Publisher
ElsevierISSN
0167-8655Type
info:eu-repo/semantics/articlePublisher version
http://ac.els-cdn.com/S0167865512002887/1-s2.0-S0167865512002887-main.pdf?_tid=f ...Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
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Pattern Recognition Letters, 2013, Marzo, Vol. 34, nº 4Rights
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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- LSI_Articles [362]