RT Journal Article
T1 Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics
A1 Lopez-Garcia, Pedro
A1 Masegosa, Antonio D.
A1 Osaba, Eneko
A1 Onieva, Enrique
A1 Perallos, Asier
AB One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature.
SN 0924-669X
YR 2019
FD 2019-08-15
LA eng
NO Lopez-Garcia , P , Masegosa , A D , Osaba , E , Onieva , E & Perallos , A 2019 , ' Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics ' , Applied Intelligence , vol. 49 , no. 8 , pp. 2807-2822 . https://doi.org/10.1007/s10489-019-01423-6
NO Publisher Copyright: © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
NO This work has been supported by the research projects TEC2013-45585-C2-2-R and TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness, the TIMON project, which received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 636220, and the LOGISTAR project, which received funding from European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769142. This work has been supported by the research projects TEC2013-45585-C2-2-R and TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness, the TIMON project, which received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 636220, and the LOGISTAR project, which received funding from European Union?s Horizon 2020 research and innovation programme under grant agreement No. 769142.
DS TECNALIA Publications
RD 3 sept 2024