Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection
Yazarlar (2)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Doç. Dr. Ümit Atila Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale)
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| Dergi Adı | Academic Platform Journal of Engineering and Science | ||
| Dergi ISSN | 2147-4575 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 31-05-2020 |
| Cilt / Sayı / Sayfa | 8 / 2 / 279–285 | DOI | 10.21541/apjes.569553 |
| Makale Linki | https://doi.org/10.21541/apjes.569553 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| Özet |
| Epilepsy disease, a neurological disorder that causesrecurrent and sudden crises, occurs at unforeseen times. This study presentsthe classification of electroencephalogram signals for epileptic seizureprediction. The performances of the machine learning algorithms are evaluatedon the dataset extracted from electroencephalogram signals. The datasetconsists of 500 instances which have 4097 data points for 23.5 seconds. Sincethe dataset unbalanced, Random Under Sampling and Random Over Sampling methodsare performed on this dataset. Therefore, this study is conducted on threedatasets. Each dataset is split to 60% train - 40% test, 70% train - 30% testand 80% train - 20% test within the three scenarios. The performances ofDiagonal Linear Discriminant Analysis, Linear Discriminant Analysis, LogisticRegression and Random Forest machine learning algorithms on these datasets areassessed, and discussed. The overall results show that Random Forest is thesuperior algorithm for all datasets in terms of accuracy, sensitivity and specificitymetrics. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 4 |