Abstract
The negative impact on learning associated with imbalanced proportion of classes has exploded lately with the exponential growth of “cheap” data. Many real-world problems present scarce number of instances in one class whereas in others their cardinality is several factors greater. The current techniques that treat large-scale imbalanced data are focused on obtaining fast, scalable, and parallel sampling techniques following the standard MapReduce procedure. These generate local balanced solutions in each map, which are eventually combined into a final set. Nevertheless, as we will see later, this divide-and-conquer strategy entails several problems, such as small disjuncts, data lack, etc. In this chapter we also review the latest proposals on imbalanced Big Data preprocessing and present a MapReduce framework for imbalanced preprocessing which includes several state-of-the-art sampling techniques.
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Luengo, J., García-Gil, D., Ramírez-Gallego, S., García, S., Herrera, F. (2020). Imbalanced Data Preprocessing for Big Data. In: Big Data Preprocessing. Springer, Cham. https://doi.org/10.1007/978-3-030-39105-8_8
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DOI: https://doi.org/10.1007/978-3-030-39105-8_8
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