Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/13827
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kuntalp, Mehmet | - |
dc.contributor.author | Düzyel, Okan | - |
dc.date.accessioned | 2023-10-03T07:16:29Z | - |
dc.date.available | 2023-10-03T07:16:29Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://doi.org/10.1016/j.eswa.2023.121199 | - |
dc.identifier.uri | https://hdl.handle.net/11147/13827 | - |
dc.description.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 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Expert Systems with Applications | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Data augmentation | en_US |
dc.subject | ECG | en_US |
dc.subject | Generative adversarial neural networks | en_US |
dc.subject | t-SNE | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.title | A new method for GAN-based data augmentation for classes with distinct clusters | en_US |
dc.type | Article | en_US |
dc.authorid | 0000-0002-9123-3146 | - |
dc.institutionauthor | Düzyel, Okan | - |
dc.department | İzmir Institute of Technology. Electrical and Electronics Engineering | en_US |
dc.identifier.volume | 235 | en_US |
dc.identifier.wos | WOS:001066101400001 | en_US |
dc.identifier.scopus | 2-s2.0-85168424458 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1016/j.eswa.2023.121199 | - |
dc.authorscopusid | 56247263600 | - |
dc.authorscopusid | 58135677500 | - |
dc.identifier.scopusquality | Q1 | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | embargo_20260101 | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
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 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
1-s2.0-S0957417423017013-main.pdf Until 2026-01-01 | 5.41 MB | Adobe PDF | View/Open Request a copy |
CORE Recommender
WEB OF SCIENCETM
Citations
1
checked on May 10, 2024
Page view(s)
28
checked on May 6, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.