Surrounding neighborhood-based SMOTE for learning from imbalanced data sets
Scholar | Other documents of the author: García Jiménez, Vicente; Sánchez Garreta, José Salvador; Martín Félez, Raúl; Mollineda Cárdenas, Ramón A.
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TitleSurrounding neighborhood-based SMOTE for learning from imbalanced data sets
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. [-]
© Springer-Verlag Berlin Heidelberg 2012. "The final publication is available at link.springer.com"
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