Emerging SMOTE and GAN Variants for Data Augmentation in Imbalance Machine Learning Tasks: A Review

Emerging SMOTE and GAN Variants for Data Augmentation in Imbalance Machine Learning Tasks: A Review

Class imbalance is a pervasive challenge in real-world machine learning (ML) applications, where the minority class, often the class of interest, is significantly underrepresented. This imbalance can degrade model performance, result in misleading evaluation metrics, and complicate validation proces...

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Bibliographic Details
Main Authors: Amadi G. Udu, Marwah T. Salman, Maryam K. Ghalati, Andrea Lecchini-Visintini, David R. Siddle, Hongbiao Dong
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Class imbalance
data-augmentation
generative adversarial networks
machine learning
SMOTE
Online Access:https://ieeexplore.ieee.org/document/11062634/
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