Surrounding neighborhood-based SMOTE for learning from imbalanced data sets
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Other documents of the author: García, Vicente; Sánchez Garreta, Josep Salvador; Martín Félez, Raúl; Mollineda, Ramón A.
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
Surrounding neighborhood-based SMOTE for learning from imbalanced data setsAuthor (s)
Date
2012Publisher
SpringerType
info:eu-repo/semantics/articlePublisher version
http://link.springer.com/content/pdf/10.1007%2Fs13748-012-0027-5.pdfVersion
info:eu-repo/semantics/acceptedVersionSubject
Abstract
Many traditional approaches to pattern classifi-
cation assume that the problem classes share similar prior
probabilities. However, in many real-life applications, this
assumption is grossly violated. Often, the ... [+]
Many traditional approaches to pattern classifi-
cation assume that the problem classes share similar prior
probabilities. However, in many real-life applications, this
assumption is grossly violated. Often, the ratios of prior probabilities between classes are extremely skewed. This situation
is known as the class imbalance problem. One of the strategies to tackle this problem consists of balancing the classes
by resampling the original data set. The SMOTE algorithm
is probably the most popular technique to increase the size of
the minority class by generating synthetic instances. From the
idea of the original SMOTE, we here propose the use of three
approaches to surrounding neighborhood with the aim of
generating artificial minority instances, but taking into
account both the proximity and the spatial distribution of the
examples. Experiments over a large collection of databases
and using three different classifiers demonstrate that the new
surrounding neighborhood-based SMOTE procedures
significantly outperform other existing over-sampling algorithms. [-]
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Prog Artif Intell (2012) 1:347–362Rights
© Springer-Verlag Berlin Heidelberg 2012. "The final publication is available at link.springer.com"
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