KDE-Based Ensemble Learning for Imbalanced Data

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorMoussa, Sherif
dc.contributor.authorAvante Reyes, Jorge
dc.date.accessioned2022-10-10T10:51:53Z
dc.date.available2022-10-10T10:51:53Z
dc.date.copyright© 2022
dc.date.issued2022-09
dc.description.abstractImbalanced class distribution affects many applications in machine learning, including medical diagnostics, text classification, intrusion detection and many others. In this paper, we propose a novel ensemble classification method designed to deal with imbalanced data. The proposed method trains each tree in the ensemble using uniquely generated synthetically balanced data. The data balancing is carried out via kernel density estimation, which offers a natural and effective approach to generating new sample points. We show that the proposed method results in a lower variance of the model estimator. The proposed method is tested against benchmark classifiers on a range of simulated and real-life data. The results of experiments show that the proposed classifier significantly outperforms the benchmark methods. © 2022 by the authors.
dc.identifier.citationKamalov, F., Moussa, S., & Avante Reyes, J. (2022). KDE-based ensemble learning for imbalanced data. Electronics (Switzerland), 11(17). https://doi.org/10.3390/electronics11172703.
dc.identifier.issn20799292
dc.identifier.urihttps://doi.org/10.3390/electronics11172703
dc.identifier.urihttp://hdl.handle.net/20.500.12519/710
dc.language.isoen_US
dc.publisherMDPI
dc.relation.ispartofseriesElectronics (Switzerland); Volume 11, Issue 17
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.holderCopyright : © 2022 by the authors.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdata sampling
dc.subjectensemble method
dc.subjectimbalanced data
dc.subjectkernel density estimate
dc.titleKDE-Based Ensemble Learning for Imbalanced Data
dc.typeArticle

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