Hybrid oversampling technique for imbalanced pattern recognition: Enhancing performance with Borderline Synthetic Minority oversampling and Generative Adversarial Networks

Hybrid oversampling technique for imbalanced pattern recognition: Enhancing performance with Borderline Synthetic Minority oversampling and Generative Adversarial Networks

Class imbalance problems (CIP) are one of the potential challenges in developing unbiased Machine Learning models for predictions. CIP occurs when data samples are not equally distributed between two or multiple classes. Several synthetic oversampling techniques have been introduced to balance the i...

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Bibliographic Details
Main Authors: Md Manjurul Ahsan, Shivakumar Raman, Yingtao Liu, Zahed Siddique
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Machine Learning with Applications
Subjects:
Imbalanced class
Generative Adversarial Networks
Synthetic Minority Oversampling Technique
Borderline Synthetic Minority oversampling
Machine learning
Oversampling
Online Access:http://www.sciencedirect.com/science/article/pii/S2666827025000209
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