Ensemble Learning with Resampling for Imbalanced Data

dc.contributor.authorKamalov, Firuz
dc.contributor.authorElnagar, Ashraf
dc.contributor.authorLeung, Ho Hon
dc.date.accessioned2021-10-17T08:31:18Z
dc.date.available2021-10-17T08:31:18Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2021), available online at: https://doi.org/10.1007/978-3-030-84529-2_48en_US
dc.description.abstractImbalanced class distribution is an issue that appears in various applications. In this paper, we undertake a comprehensive study of the effects of sampling on the performance of bootstrap aggregating in the context of imbalanced data. Concretely, we carry out a comparison of sampling methods applied to single and ensemble classifiers. The experiments are conducted on simulated and real-life data using a range of sampling methods. The contributions of the paper are twofold: i) demonstrate the effectiveness of ensemble techniques based on resampled data over a single base classifier and ii) compare the effectiveness of different resampling techniques when used during the bagging stage for ensemble classifiers. The results reveal that ensemble methods overwhelmingly outperform single classifiers based on resampled data. In addition, we discover that NearMiss and random oversampling (ROS) are the optimal sampling algorithms for ensemble learning. © 2021, Springer Nature Switzerland AG.en_US
dc.identifier.citationKamalov, F., Elnagar, A., & Leung, H. H. (2021). Ensemble Learning with Resampling for Imbalanced Data. In D.-S. Huang, K.-H. Jo, J. Li, V. Gribova, & A. Hussain (Eds.), Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12837 (pp. 564-578). Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_48en_US
dc.identifier.isbn978-303084528-5
dc.identifier.issn03029743
dc.identifier.urihttps://doi.org/10.1007/978-3-030-84529-2_48
dc.identifier.urihttp://hdl.handle.net/20.500.12519/452
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relationAuthors Affiliations : Kamalov, F., Faculty of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Elnagar, A., Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates; Leung, H.H., Department of Mathematics, UAE University, Al Ain, United Arab Emirates
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);Volume 12837 LNCS
dc.rightsPermission to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2021, Springer Nature Switzerland AG.
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=203b426d-1e19-4147-8ad6-a4e839676bd7
dc.subjectData preprocessing samplingen_US
dc.subjectEnsemble methoden_US
dc.subjectImbalanced dataen_US
dc.subjectOversamplingen_US
dc.subjectUndersamplingen_US
dc.titleEnsemble Learning with Resampling for Imbalanced Dataen_US
dc.typeOtheren_US
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