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Data Level Preprocessing Methods

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Book cover Learning from Imbalanced Data Sets

Abstract

The first mechanism to address the problem of imbalanced learning was the use of sampling methods. They consists of modifying a set of imbalanced data using different procedures to provide a balanced or more adequate data distribution to the subsequent learning tasks. In the specialized literature, many studies have shown that, for several types of classifiers, rebalancing the dataset significantly improves the overall performance of the classification compared to a non-preprocessed data set. Over the years, this procedure has been common and the use of sampling methods for imbalanced learning has been standardized. Still, classifiers do not always have to use this kind of preprocessing because many of them are able to directly deal with imbalanced datasets. There is no clear rule that tells us which strategy is best, whether to adapt the behavior of learning algorithms or to use data preprocessing techniques. However, data sampling and preprocessing techniques are standard techniques in imbalanced learning, they are widely used in Data Science problems. They are simple and easily configurable and can be used in synergy with any learning algorithm. This chapter will review the techniques of sampling, undersampling (the classical ones in Sect. 5.2 and advanced approaches in Sect. 5.3) and oversampling such as SMOTE in Sect. 5.4, as well as the most-known algorithm SMOTE and its derivatives in Sect. 5.5. Some hybridizations of undersampling and oversampling are described in Sect. 5.6. Experiments with graphical illustrations will be carried out to show the behavior of these techniques.

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Notes

  1. 1.

    We would like to thank Sergio González for the development of a visualization module for this task. See more at https://github.com/sergiogvz/imbalanced_synthetic_data_plots

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Authors and Affiliations

  1. Department of Computer Science and AI, University of Granada, Granada, Granada, Spain

    Alberto Fernández & Salvador García

  2. Institute of Smart Cities, Public University of Navarre, Pamplona, Spain

    Mikel Galar

  3. Department of Computer Science, Universidade Federal do ABC, Santo Andre, Brazil

    Ronaldo C. Prati

  4. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA

    Bartosz Krawczyk

  5. Department of Computer Science and AI, University of Granada, Granada, Spain

    Francisco Herrera

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  1. Alberto Fernández
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  2. Salvador García
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Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F. (2018). Data Level Preprocessing Methods. In: Learning from Imbalanced Data Sets. Springer, Cham. https://doi.org/10.1007/978-3-319-98074-4_5

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