Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/13827
Title: | A new method for GAN-based data augmentation for classes with distinct clusters | Authors: | Kuntalp, Mehmet Düzyel, Okan |
Keywords: | Data augmentation ECG Generative adversarial neural networks t-SNE Convolutional neural networks |
Publisher: | Elsevier | Abstract: | Data augmentation is a commonly used approach for addressing the issue of limited data availability in machine learning. There are various methods available, including classical and modern techniques. However, when applying modern data augmentation methods, such as Generative Adversarial Neural Networks (GANs), to a class specific data, the resulting data can exhibit structural discrepancies. This study explores a different use of GANs as a data augmentation method that solves this problem using the electrocardiogram (ECG) signals in the MIT-BIH arrhythmia dataset as the example. We begin by examining the cluster structure of a specific class using t-Distributed Stochastic Neighbor (t-SNE) method. Based on this cluster structure, we propose a new method for applying GANs to augment data for that class. We assess the effect of our method in a classification task using 1-D Convolutional Neural Network (CNN), Support Vector Machine (SVM), One vs one classifier (Ovo), K-Nearest Neighbors (KNN), and Random Forest as the classifiers. The results demonstrate that our proposed method could lead to better classification performance if a specific class has distinct clusters when compared to normal use of GANs. © 2023 Elsevier Ltd | URI: | https://doi.org/10.1016/j.eswa.2023.121199 https://hdl.handle.net/11147/13827 |
ISSN: | 0957-4174 |
Appears in Collections: | Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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1-s2.0-S0957417423017013-main.pdf Until 2026-01-01 | 5.41 MB | Adobe PDF | View/Open Request a copy |
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