A New Classification Approach Based On Support Vector Regression For Epileptic Seizure Detection

Year 2024, Volume: 27 Issue: 2, 587 - 601, 27.03.2024

Esra Betül Kınacı , Hasan Bal , Harun Kınacı

https://doi.org/10.2339/politeknik.1055549

Abstract

Although the classification problem is a subject that has been studied by researchers for a long time, it is still up-to-date. Especially the problems that image processing and diagnosis of disease are some of the most current application topics. This study presents a new data classification method based on support vector regression and mathematical programming. The proposed method consists of a two-stage hybrid structure. In the first step, the classification score is obtained for each unit with the support vector regression equation. In the second stage, using the classification scores of the units, a classification rule is created with the help of a mathematical model and the classification of the units is provided. The proposed method offers an alternative innovation to traditional methods. Methods based on traditional mathematical programming separate classes with a linear function. This situation limits the use of algorithms based on mathematical programming. The proposed method can be used in all linear or non-linearly separable data structures, as well as easily transforming into problem types with more than two groups. The model is applied to the classification problem of Electroencephalograph (EEG) signals and the classification performance is compared with the existing methods. The results obtained are given in the tables and it is shown that the proposed model can be an alternative to the existing algorithms

Keywords

Support Vector Machines, Support Vector Regression, Classification, Bonn Dataset

Epileptik Nöbet Tespiti İçin Destek Regresyon Temelli Yeni Bir Sınıflandırma Yaklaşımı

Abstract

Sınıflandırma problemi araştırmacılar tarafından uzun zamandır incelenen bir konu olmasına rağmen güncelliğini hala korumaktadır. Özellikle görüntü işleme ve hastalık tanısının belirlenmesi problemleri güncel uygulama alanlarından bazılardır. Bu çalışma destek vektör regresyon ve matematiksel programlamaya dayalı yeni bir veri sınıflandırma yöntemi sunmaktadır. Önerilen yöntem iki aşamalı hibrit bir yapıdan oluşmaktadır. İlk aşamada, destek vektör regresyon denklemi ile her bir birim için sınıflandırma skoru elde edilirken ikinci aşamada ise birimlerin sınıflandırma skorları kullanılarak bir matematiksel model yardımıyla sınıflandırma kuralı oluşturulur ve birimlerin sınıflandırılması sağlanır. Önerilen yöntem geleneksel yöntemlere alternatif bir yenilik sunmaktadır. Geleneksel matematiksel programlamaya dayalı yöntemler sınıfları doğrusal bir fonksiyon ile ayırır. Bu durum ise matematiksel programlamaya dayalı algoritmalarının kullanımını kısıtlar. Önerilen yöntem, doğrusal veya doğrusal ayrılamayan veri yapılarının tamamında kullanılabilir olmasının yanı sıra ikiden fazla grup sayısının olduğu problem türlerine de kolaylıkla dönüştürülebilmektedir. Model önce simülasyon ile irdelenmiş sonrasında Elektroensefalograf (EEG) sinyallerinin sınıflandırılması probleminde uygulanmış ve sınıflandırma performansı mevcut yöntemlerle karşılaştırılmıştır. Elde edilen sonuçlar tablolarda verilmiş ve önerilen modelin mevcut algoritmalara alternatif olabileceğini gösterilmiştir.

Keywords

Destek Vektör Makineleri, Destek Vektör Regresyon, Sınıflandırma, Bonn Veri Seti

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