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SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification

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Abstract

Nowadays, it is usual to work with large amounts of data since our capacity of collecting and storing information has increased significantly. The extraction of knowledge from these scenarios is commonly known as “Big Data,” and it is performed on large clusters with MapReduce platforms. Imbalanced classification poses a problem both in traditional and Big Data learning scenarios. Data sampling is one of the ways that allows to improve the performance on imbalanced problems. A commodity hardware-based method for Big Data problems can offload these computations from the expensive and highly demanded hardware that MapReduce platforms require. The characteristics of some sampling methods make them suitable to be adapted to commodity hardware, taking advantage of the parallel computation capabilities of graphics processing units. SMOTE is one of the most popular oversampling methods which is based on the nearest neighbor rule. The proposed SMOTE-GPU efficiently handles large datasets (several millions of instances) on a wide variety of commodity hardware, including a laptop computer.

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  1. http://sci2s.ugr.es/GPU-SME-kNN.

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Acknowledgements

This work was supported by the Spanish National Research Projects TIN2013-47210-P, TIN2014-57251-P and TIN2016-81113-R and by the Andalusian Regional Government Excellence Research Project P12-TIC-2958. P.D. Gutiérrez holds an FPI scholarship from the Spanish Ministry of Economy and Competitiveness (BES-2012-060450).

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, CITIC-UGR, University of Granada, 18071, Granada, Spain

    Pablo D. Gutiérrez, José M. Benítez & Francisco Herrera

  2. Department of Software Engineering, CITIC-UGR, University of Granada, 18071, Granada, Spain

    Miguel Lastra

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  1. Pablo D. Gutiérrez
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  2. Miguel Lastra
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  3. José M. Benítez
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  4. Francisco Herrera
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Correspondence to Pablo D. Gutiérrez.

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Gutiérrez, P.D., Lastra, M., Benítez, J.M. et al. SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification. Prog Artif Intell 6, 347–354 (2017). https://doi.org/10.1007/s13748-017-0128-2

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