Gamma distribution-based sampling for imbalanced data

dc.contributor.author Kamalov, Firuz
dc.contributor.author Denisov, Dmitry
dc.date.accessioned 2021-03-24T06:15:51Z
dc.date.available 2021-03-24T06:15:51Z
dc.date.copyright © 2020
dc.date.issued 2020-11-05
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Knowledge-Based Systems (2020), available online at: https://doi.org/10.1016/j.knosys.2020.106368 en_US
dc.description.abstract Imbalanced class distribution is a common problem in a number of fields including medical diagnostics, fraud detection, and others. It causes bias in classification algorithms leading to poor performance on the minority class data. In this paper, we propose a novel method for balancing the class distribution in data through intelligent resampling of the minority class instances. The proposed method is based on generating new minority instances in the neighborhood of the existing minority points via a gamma distribution. Our method offers a natural and coherent approach to balancing the data. We conduct a comprehensive numerical analysis of the new sampling technique. The experimental results show that the proposed method outperforms the existing state-of-the-art methods for imbalanced data. Concretely, the new sampling technique produces the best results on 12 out of 24 real life as well as synthetic datasets. For comparison, the SMOTE method achieves the top score on only 1 dataset. We conclude that the new technique offers a simple yet effective sampling approach to balance data. © 2020 Elsevier B.V. en_US
dc.identifier.citation Kamalov, F., & Denisov, D. (2020). Gamma distribution-based sampling for imbalanced data. Knowledge-Based Systems, 207, 106368. https://doi.org/10.1016/j.knosys.2020.106368 en_US
dc.identifier.issn 09507051
dc.identifier.uri https://doi.org/10.1016/j.knosys.2020.106368
dc.identifier.uri http://hdl.handle.net/20.500.12519/354
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation Authors Affiliations : Kamalov, F., Canadian University Dubai, United Arab Emirates; Denisov, D., Careem, United Arab Emirates
dc.relation.ispartofseries Knowledge-Based Systems;Volume 207
dc.rights License to reuse the abstract has been secured from Elsevier and Copyright Clearance Center.
dc.rights.holder Copyright : © 2020 Elsevier B.V.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=49be548d-b52a-48fd-896b-1ae013f76b48
dc.subject Gamma distribution en_US
dc.subject Imbalanced data en_US
dc.subject Sampling en_US
dc.subject Diagnosis en_US
dc.subject Class distributions en_US
dc.subject Classification algorithm en_US
dc.subject Imbalanced class en_US
dc.subject Medical diagnostics en_US
dc.subject Sampling technique en_US
dc.subject State-of-the-art methods en_US
dc.subject Synthetic datasets en_US
dc.subject Probability distributions en_US
dc.title Gamma distribution-based sampling for imbalanced data en_US
dc.type Article en_US
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