Resampling methods versus cost functions for training an MLP in the class imbalance context
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Other documents of the author: Alejo Eleuterio, Roberto; Martínez Sotoca, José; Valdovinos Rosas, Rosa María; Gasca, Eduardo; Toribio Luna, Primitivo
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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http://dx.doi.org/10.1007/978-3-642-21090-7 |
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Title
Resampling methods versus cost functions for training an MLP in the class imbalance contextAuthor (s)
Date
2011Publisher
Springer-VerlagISSN
0302-9743Type
info:eu-repo/semantics/articlePublisher version
http://www.springerlink.com/content/54n6g275w235v6uk/fulltext.pdfSubject
Abstract
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, 19-26Rights
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