RT info:eu-repo/semantics/article
T1 Experimental evaluation of ensemble classifiers for imbalance in Big Data
A1 Juez Gil, Mario
A1 Arnaiz González, Álvar
A1 Rodríguez Diez, Juan José
A1 García Osorio, César
K1 Unbalance
K1 Imbalance
K1 Ensemble
K1 Resampling
K1 Big Data
K1 Spark
K1 Informática
K1 Computer science
AB Datasets are growing in size and complexity at a pace never seen before, forming ever larger datasets known as Big Data. A common problem for classification, especially in Big Data, is that the numerous examples of the different classes might not be balanced. Some decades ago, imbalanced classification was therefore introduced, to correct the tendency of classifiers that show bias in favor of the majority class and that ignore the minority one. To date, although the number of imbalanced classification methods have increased, they continue to focus on normal-sized datasets and not on the new reality of Big Data. In this paper, in-depth experimentation with ensemble classifiers is conducted in the context of imbalanced Big Data classification, using two popular ensemble families (Bagging and Boosting) and different resampling methods. All the experimentation was launched in Spark clusters, comparing ensemble performance and execution times with statistical test results, including the newest ones based on the Bayesian approach. One very interesting conclusion from the study was that simpler methods applied to unbalanced datasets in the context of Big Data provided better results than complex methods. The additional complexity of some of the sophisticated methods, which appear necessary to process and to reduce imbalance in normal-sized datasets were not effective for imbalanced Big Data.
PB Elsevier
SN 1568-4946
YR 2021
FD 2021-09
LK http://hdl.handle.net/10259/5766
UL http://hdl.handle.net/10259/5766
LA eng
NO “la Caixa” Foundation, Spain, under agreement LCF/PR/PR18/51130007. This work was supported by the Junta de Castilla y León, Spain under project BU055P20 (JCyL/FEDER, UE) co-financed through European Union FEDER funds, and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund, Spain through a pre-doctoral grant (EDU/1100/2017).
DS Repositorio Institucional de la Universidad de Burgos
RD 24-nov-2024