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Obiekt
Tytuł: A Gaussian-based WGAN-GP oversampling approach for solving the class imbalance problem
Współtwórca:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Tytuł publikacji grupowej:
Abstract:
In practical applications of machine learning, the class distribution of the collected training set is usually imbalanced, i.e., there is a large difference among the sizes of different classes. The class imbalance problem often hinders the achievable generalization performance of most classifier learning algorithms to a large extent. To ameliorate the learning performance, some effective approaches have been proposed in the literature, where the recently presented GAN-based oversampling methods are very representative. However, their generated minority class examples have the risk of high similarity and duplication degree. ; To further ameliorate the quality of the generated minority class examples, i.e., to make the generated examples effectively expand the minority class region, a novel oversampling approach named the GWGAN-GP is proposed, which is based on the Gaussian distribution label within the framework of a Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Our GWGAN-GP approach incorporates the Gaussian distribution as an input label, thereby making the generated examples more diverse and dispersive. ; The examples are then combined with the original dataset to form a balanced dataset, which is subsequently utilized to evaluate the classification performance of three selected classification algorithms. Experimental results on 16 imbalanced datasets demonstrate that the GWGAN-GP not only generates examples that better conform to the distribution of the original dataset, but also achieves superior classification performance. Specifically, when combined with the KNN classifier, the GWGAN-GP significantly outperforms other oversampling approaches considered in the study.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Identyfikator zasobu:
DOI:
Strony:
Źródło:
AMCS, volume 34, number 2 (2024) ; kliknij tutaj, żeby przejść
Jezyk:
Licencja CC BY 4.0:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2024)
Data ostatniej modyfikacji:
Jul 29, 2025
Data dodania obiektu:
Jul 29, 2025
Liczba wyświetleń treści obiektu:
17
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/repozytorium/publication/101673
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 1 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 2 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 3 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 4 (2024)
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| Nazwa wydania | Data |
|---|---|
| A Gaussian-based WGAN-GP oversampling approach for solving the class imbalance problem | Jul 29, 2025 |
Obiekty Podobne
Luo, Haoqi Wan, Liang Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Koziarski, Michał Woźniak, Michał Stefanowski, Jerzy - ed. Krawiec, Krzysztof - ed. Wrembel, Robert - ed.
Tenne, Yoel Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Blachnik, Marcin Gamper, Johann - ed. Wrembel, Robert - ed.
Żelasko, Dariusz Pławiak, Paweł Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Papież, Anna Badie, Christophe Polanska, Joanna Gamper, Johann - ed. Wrembel, Robert - ed.
Czarnowski, Ireneusz Jędrzejowicz, Piotr Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Troć, Maciej Unold, Olgierd Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
