Classification of Gene Samples Using Pair-Wise Support Vector Machines

Year 2017, Volume 5, Issue 2, 2017, 283 - 292, 29.11.2017

Engin Taş

https://doi.org/10.17093/alphanumeric.345115

Cited By: 1

Abstract

The main problem in the classification problems encountered with gene samples is that the dimension of the data is high although the sample size is small. In such problems, the classifier to be used must be a classifier that allows the processing of high dimensional data and extracts maximum information from a small number of samples at hand. In this context, a classification methodology has been developed, which first transforms the problem of binary or multiple classification into separate pair-wise classification problems. To this end, an online classifier has been adapted to solve pair-wise binary classification problems. The resulting classifier performed better on most of the real problems compared to other popular classifiers.

Keywords

Tumor classification, pair-wise classification, support vector machine, kernel methods

Gen Örneklerinin Eşli Destek Vektör Makinesi ile Sınıflandırılması

Abstract

Gen örnekleriyle ilgili karşılaşılan sınıflandırma problemlerinde en büyük sorun az sayıda örnek elde edilmesine karşın verinin büyük boyutlu olmasıdır. Bu tür problemlerde kullanılacak sınıflandırıcının büyük boyutlu verinin işlenmesine olanak sağlayan ve eldeki az sayıda örnekten maksimum bilgiyi çıkaran bir sınıflandırıcı olması gerekir. Bu kapsamda, öncelikle ikili/çoklu sınıflandırma problemlerini ayrı ayrı eşli ikili sınıflandırma problemlerine çeviren bir sınıflandırma metodolojisi geliştirilmiştir. Bunun için, çevrimiçi bir sınıflandırıcı eşli ikili sınıflandırma problemlerini çözecek şekilde tekrar düzenlenmiştir. Oluşan sınıflandırıcı gerçek problemlerin çoğu üzerinde diğer popüler sınıflandırıcılara göre oldukça iyi bir performans göstermiştir.

Keywords

Tümör sınıflandırması, eşli sınıflandırma, destek vektör makinesi, çekirdek yöntemler

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