2026-04-17T23:14:08Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/62062022-11-21T12:51:46Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378
Repositorio Institucional de la Universidad de Burgos
author
Juez Gil, Mario
author
Arnaiz González, Álvar
author
Rodríguez Diez, Juan José
author
López Nozal, Carlos
author
García Osorio, César
2021-11-23T08:25:06Z
2021-11-23T08:25:06Z
2021-11
0925-2312
http://hdl.handle.net/10259/6206
10.1016/j.neucom.2021.08.086
One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an acceptable execution time. In this paper we present Approx-SMOTE, a parallel implementation of the SMOTE algorithm for the Apache Spark framework. The key difference with the original SMOTE, besides parallelism, is that it uses an approximated version of k-Nearest Neighbor which makes it highly scalable. Although an implementation of SMOTE for Big Data already exists (SMOTE-BD), it uses an exact Nearest Neighbor search, which does not make it entirely scalable. Approx-SMOTE on the other hand is able to achieve up to 30 times faster run times without sacrificing the improved classification performance offered by the original SMOTE.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
SMOTE
Imbalance
Spark
Big data
Data mining
Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/6206/1/Juez-neurocomputing_2021.pdf
File
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Juez-neurocomputing_2021.pdf