Kernel density estimation-based sampling for neural network classification

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorElnagar, Ashraf
dc.date.accessioned2022-05-18T12:48:48Z
dc.date.available2022-05-18T12:48:48Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2021 International Symposium on Networks, Computers and Communications, ISNCC (2021), available online at: https://doi.org/10.1109/ISNCC52172.2021.9615715
dc.description.abstractImbalanced data occurs in a wide range of scenarios. The skewed distribution of the target variable elicits bias in machine learning algorithms. One of the popular methods to combat imbalanced data is to artificially balance the data through resampling. In this paper, we compare the efficacy of a recently proposed kernel density estimation (KDE) sampling technique in the context of artificial neural networks. We benchmark the KDE sampling method against two base sampling techniques and perform comparative experiments using 8 datasets and 3 neural networks architectures. The results show that KDE sampling produces the best performance on 6 out of 8 datasets. However, it must be used with caution on image datasets. We conclude that KDE sampling is capable of significantly improving the performance of neural networks. © 2021 IEEE.
dc.identifier.citationKamalov, F., & Elnagar, A. (2021). Kernel density estimation-based sampling for neural network classification. 2021 International Symposium on Networks, Computers and Communications, ISNCC. https://doi.org/10.1109/ISNCC52172.2021.9615715
dc.identifier.isbn978-073811316-6
dc.identifier.urihttps://doi.org/10.1109/ISNCC52172.2021.9615715
dc.identifier.urihttp://hdl.handle.net/20.500.12519/639
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationAuthors Affiliations : Kamalov, F., Canadian University Dubai, Department Of Electrical Engineering, Dubai, United Arab Emirates; Elnagar, A., University Of Sharjah, Department Of Computer Science, Sharjah, United Arab Emirates
dc.relation.ispartofseries2021 International Symposium on Networks, Computers and Communications, ISNCC
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectDeep learning
dc.subjectImbalanced data
dc.subjectKDE
dc.subjectKernel density estimation
dc.subjectNeural networks
dc.subjectSampling
dc.titleKernel density estimation-based sampling for neural network classification
dc.typeConference Paper
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