Abstract
Informed pre-processing methods for improving classifiers learned from class-imbalanced data are considered. We discuss different ways of analyzing the characteristics of local distributions of examples in such data. Then, we experimentally compare main informed pre-processing methods and show that identifying types of minority examples depending on their k nearest neighbourhood may help in explaining differences in performance of these methods. Finally, we exploit the information about the local neighbourhood to modify the oversampling ratio in a SMOTE–related method.
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Stefanowski, J., Napierała, K., Trzcielińska, M. (2014). Local Characteristics of Minority Examples in Pre-processing of Imbalanced Data. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_13
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DOI: https://doi.org/10.1007/978-3-319-08326-1_13
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