Progress in Artificial Intelligence

, Volume 6, Issue 4, pp 347–354 | Cite as

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

  • Pablo D. Gutiérrez
  • Miguel Lastra
  • José M. Benítez
  • Francisco Herrera
Regular Paper


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.


Imbalanced classification SMOTE CUDA Big Data 



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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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Artificial Intelligence, CITIC-UGRUniversity of GranadaGranadaSpain
  2. 2.Department of Software Engineering, CITIC-UGRUniversity of GranadaGranadaSpain

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