Resampling methods versus cost functions for training an MLP in the class imbalance context
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Otros documentos de la autoría: Alejo Eleuterio, Roberto; Martínez Sotoca, José; Valdovinos Rosas, Rosa María; Gasca, Eduardo; Toribio Luna, Primitivo
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
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Título
Resampling methods versus cost functions for training an MLP in the class imbalance contextAutoría
Fecha de publicación
2011Editor
Springer-VerlagISSN
0302-9743Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.springerlink.com/content/54n6g275w235v6uk/fulltext.pdfPalabras clave / Materias
Resumen
The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process. Both methods try to compensate ... [+]
The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process. Both methods try to compensate the class
imbalance distribution, however, it is necessary to consider the effect that each
method produces in the training process of the Multilayer Perceptron (MLP). The
experimental results shows the negative and positive effects that each of these
approaches has on the MLP behavior [-]
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Lecture notes in computer science (2011), vol. 6676, 19-26Derechos de acceso
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