Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection

Year 2020, Volume: 8 Issue: 2, 279 - 285, 26.05.2020

Kemal Akyol , ÜMİT Atila

https://doi.org/10.21541/apjes.569553

Cited By: 1

Abstract

Epilepsy disease, a neurological disorder that causes
recurrent and sudden crises, occurs at unforeseen times. This study presents
the classification of electroencephalogram signals for epileptic seizure
prediction. The performances of the machine learning algorithms are evaluated
on the dataset extracted from electroencephalogram signals. The dataset
consists of 500 instances which have 4097 data points for 23.5 seconds. Since
the dataset unbalanced, Random Under Sampling and Random Over Sampling methods
are performed on this dataset. Therefore, this study is conducted on three
datasets. Each dataset is split to 60% train - 40% test, 70% train - 30% test
and 80% train - 20% test within the three scenarios. The performances of
Diagonal Linear Discriminant Analysis, Linear Discriminant Analysis, Logistic
Regression and Random Forest machine learning algorithms on these datasets are
assessed, and discussed. The overall results show that Random Forest is the
superior algorithm for all datasets in terms of accuracy, sensitivity and specificity
metrics.

Keywords

Epileptic seizure, machine learning, unbalanced and balanced dataset, over sampling, under sampling.

Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection

Abstract

Tekrarlayan
ve ani krizlere neden olan nörolojik bir hastalık olan epilepsy hastalığı
öngörülemeyen zamanlarda ortaya çıkar. Bu çalışma, epileptik nöbet tahmini için
elektroensefalogram sinyallerinin sınıflandırılmasını sunmaktadır. Makine
öğrenme algoritmalarının performansı, elektroensefalogram sinyallerinden elde
edilen veriseti üzerinde değerlendirilmiştir. Veriseti, 23.5 saniye boyunca
4097 veri noktasına sahip 500 örnek içermektedir. Veriseti dengesiz olduğu
için, bu veri setinde Rastgele Alt Örnekleme ve Rastgele Üst Örnekleme
yöntemleri uygulanmıştır. Bu nedenle bu çalışma üç veri seti üzerinde
yürütülmüştür. Her veri seti üç senaryo çerçevesinde % 60 eğitim - % 40 test, %
70 eğitim - % 30 test ve % 80 eğitim - % 20 test verileri olarak ayrılmıştır. Bu
verisetleri üzerinde Çapraz Doğrusal Ayırt Edici Analiz, Doğrusal Ayırt Edici
Analiz, Lojistik Regresyon ve Rastgele Orman makine öğrenmesi algoritmaların
performansları değerlendirilmiş ve tartışılmıştır. Genel sonuçlar, tüm
verisetleri için Random Forest algoritmasının doğruluk, hassasiyet ve özgüllük metrikleri
açısından üstün olduğunu göstermiştir.

Keywords

Epileptik nöbet, makine öğrenmesi, dengesiz ve dengeli veri seti, üst örnekleme, alt örnekleme.

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