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Ensemble Learning with Resampling for Imbalanced Data

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dc.contributor.author Kamalov, Firuz
dc.contributor.author Elnagar, Ashraf
dc.contributor.author Leung, Ho Hon
dc.date.accessioned 2021-10-17T08:31:18Z
dc.date.available 2021-10-17T08:31:18Z
dc.date.copyright © 2021
dc.date.issued 2021
dc.identifier.citation Kamalov, 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_48 en_US
dc.identifier.isbn 978-303084528-5
dc.identifier.issn 03029743
dc.identifier.uri https://doi.org/10.1007/978-3-030-84529-2_48
dc.identifier.uri http://hdl.handle.net/20.500.12519/452
dc.description This 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_48 en_US
dc.description.abstract Imbalanced 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.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation Authors 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.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);Volume 12837 LNCS
dc.rights Permission to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=203b426d-1e19-4147-8ad6-a4e839676bd7
dc.subject Data preprocessing sampling en_US
dc.subject Ensemble method en_US
dc.subject Imbalanced data en_US
dc.subject Oversampling en_US
dc.subject Undersampling en_US
dc.title Ensemble Learning with Resampling for Imbalanced Data en_US
dc.type Other en_US
dc.rights.holder Copyright : © 2021, Springer Nature Switzerland AG.


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