Resampling methods versus cost functions for training an MLP in the class imbalance context
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Altres documents de l'autoria: Alejo Eleuterio, Roberto; Martínez Sotoca, José; Valdovinos Rosas, Rosa María; Gasca, Eduardo; Toribio Luna, Primitivo
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Títol
Resampling methods versus cost functions for training an MLP in the class imbalance contextAutoria
Data de publicació
2011Editor
Springer-VerlagISSN
0302-9743Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://www.springerlink.com/content/54n6g275w235v6uk/fulltext.pdfParaules clau / Matèries
Resum
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 [-]
Publicat a
Lecture notes in computer science (2011), vol. 6676, 19-26Drets d'accés
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