2026-04-17T20:36:53Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/62062022-11-21T12:51:46Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378

00925njm 22002777a 4500 dc Juez Gil, Mario author Arnaiz González, Álvar author Rodríguez Diez, Juan José author López Nozal, Carlos author García Osorio, César author 2021-11 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. 0925-2312 http://hdl.handle.net/10259/6206 10.1016/j.neucom.2021.08.086 SMOTE Imbalance Spark Big data Data mining Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark