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Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri

dc.contributor.author Calis, Ahmet Gokay
dc.contributor.author Ergezer, Halit
dc.date.accessioned 2025-10-06T17:40:16Z
dc.date.available 2025-10-06T17:40:16Z
dc.date.issued 2025
dc.description Isik University
dc.description.abstract In this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. © 2025 Elsevier B.V., All rights reserved.
dc.description.abstract In this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. en_US
dc.identifier.doi 10.1109/SIU66497.2025.11112012
dc.identifier.isbn 9798331566562
dc.identifier.isbn 9798331566555
dc.identifier.issn 2165-0608
dc.identifier.scopus 2-s2.0-105015460554
dc.identifier.uri https://doi.org/10.1109/SIU66497.2025.11112012
dc.language.iso tr
dc.language.iso tr en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.publisher IEEE en_US
dc.relation.ispartof -- 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- Istanbul; Isik University Sile Campus -- 211450
dc.relation.ispartof 33rd Conference on Signal Processing and Communications Applications-SIU-Annual -- Jun 25-28, 2025 -- Istanbul, Turkiye en_US
dc.relation.ispartofseries Signal Processing and Communications Applications Conference
dc.rights info:eu-repo/semantics/closedAccess
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Epilepsy en_US
dc.subject Machine Learning en_US
dc.subject SMOTE en_US
dc.subject Bagged Trees en_US
dc.subject Coarse-KNN en_US
dc.subject Artificial Neural Networks en_US
dc.title Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri
dc.title Effects of Different SMOTE Methods in Classification for Imbalance Epilepsy Dataset en_US
dc.title.alternative Effects of Different SMOTE Methods in Classification for Imbalance Epilepsy Dataset
dc.type Conference Object
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.wosid Calis, Ahmetgokay/Phf-0256-2026
gdc.author.wosid Ergezer, Halit/S-6502-2017
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gdc.description.department Çankaya University
gdc.description.department Çankaya University en_US
gdc.description.departmenttemp [Calis] Ahmet Gokay, Mekatronik Mühendisliǧi Bölümü, Çankaya Üniversitesi, Ankara, Turkey; [Ergezer] Halit, Mekatronik Mühendisliǧi Bölümü, Çankaya Üniversitesi, Ankara, Turkey
gdc.description.departmenttemp [Calis, Ahmet Gokay; Ergezer, Halit] Cankaya Univ, Mekatron Muhendisligi, Ankara, Turkiye en_US
gdc.description.endpage 4
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 1
gdc.description.woscitationindex Conference Proceedings Citation Index - Science
gdc.description.wosquality N/A
gdc.identifier.openalex W4413464769
gdc.identifier.wos WOS:001575462500140
gdc.index.type WoS
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gdc.virtual.author Ergezer, Halit
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