Abstract
Many efforts have been done recently proposing new intelligent resampling methods as a way to solve class imbalance problems; one of the main challenges of the machine learning community nowadays. Usually the purpose of these methods is to balance the classes. However, there are works in the literature showing that those methods can also be suitable to change the class distribution of not so imbalanced and even balanced databases, to a distribution different to 50% and significantly improve the outcome of the learning process. The aim of this paper is to analyse which resampling methods are the most competitive in this context. Experiments have been performed using 29 databases, 8 different resampling methods and two learning algorithms, and have been evaluated using AUC performance metric and statistical tests. The results show that SMOTE, the well-known intelligent resampling method, is one of the best candidates to be used, improving the results obtained by some of its variants that are successful in the context of class imbalance.
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Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M. (2013). Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_12
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DOI: https://doi.org/10.1007/978-3-642-40643-0_12
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