Mostrar el registro sencillo del ítem 2018-10-04T19:04:10Z 2018-10-04T19:04:10Z
dc.title SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data en
dc.type Documento de conferencia es
dcterms.abstract The volume of data in today’s applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved through intelligent model design and the use of distributed technologies. In this context, solutions based on the Spark platform have established themselves as a de facto standard. In this contribution, we focus on a very important framework within Big Data Analytics, namely classification with imbalanced datasets. The main characteristic of this problem is that one of the classes is underrepresented, and therefore it is usually more complex to find a model that identifies it correctly. For this reason, it is common to apply preprocessing techniques such as oversampling to balance the distribution of examples in classes. In this work we present SMOTE-BD, fully scalable preprocessing approach for imbalanced classification in Big Data. It is based on one of the most widespread preprocessing solutions for imbalanced classification, namely the SMOTE algorithm, which creates new synthetic instances according to the neighborhood of each example of the minority class. Our novel development is made to be independent of the number of partitions or processes created to achieve a higher degree of efficiency. Experiments conducted on different standard and Big Data datasets show the quality of the proposed design and implementation. en
dcterms.extent p. 23-28 es
dcterms.issued 2018
dcterms.language Inglés es
dcterms.license Attribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA 4.0) es
dcterms.subject big data, imbalanced classification, preprocessing, SMOTE, spark en
cic.version info:eu-repo/semantics/publishedVersion es Basgall, María José es Hasperué, Waldo es Naiouf, Marcelo es Fernández, Alberto es Herrera, Francisco es
cic.lugarDesarrollo Instituto de Investigación en Informática es
dcterms.subject.materia Ciencias de la Computación e Información es
dcterms.identifier.other handle:10915/69676 es
dcterms.isPartOf.item Actas JCC&BD 2018 es
dcterms.isPartOf.issue VI Jornadas de Cloud Computing & Big Data (JCC&BD) (La Plata, 2018) es
cic.isPeerReviewed true es
cic.isFulltext true es


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