LASSO ve Relief Özellik Seçimi Yöntemleri ile DVM,ÇKA ve RO Ağ Yapıları Kullanılarak DNA Mikroçip Gen İfadesi Veri setlerinin Sınıflandırılması

DNA Mikroçip teknolojisi, çok sayıda gen ifadesinin aynı anda gözlemlenebilmesini sağlayan özgün bir yöntemdir. Günümüzde bu gen ifadeleri birçok hastalığıı teşhis etmek için kullanılmaktadırlar. Bu çalışma iki özellik seçimi ve ağ yapısını çaprazlayarak birden çok verisetinde karşılaştırma yapmaktadır. Mikroçip verisetlerinde her bir örneğin gen sayısı çok sayıda olduğu için, bilgi kazancı en yüksek olan gen seçimi yapılmalıdır. Bu seçim için Relief ve LASSO özellik seçimi yöntemlerini kullandık. En önemli genler örnekten seçildikten sonra Destek Vektör Makinası (DVM), Çok Katmanlı Algılayıcı (ÇKA) ve Rastgele Orman (RO) gibi sıklıkla kullanılan sınıflandırıcılar kullanılarak veri sınıflandırıldı. LASSO özellik seçimi ve DVM daha önceki çalışmaları doğruluk ve eğitim hızı bakımından geride bırakmaktadır

DNAMicroarrayGeneExpressionDataClassificationUsingSVM,MLP,andRFwithFeature SelectionMethodsReliefandLASSO

DNA microarray technology is a novel method to monitor expression levels of alargenumberofgenessimultaneously. Thesegeneexpressionscanbeandarebeingused to detect various forms of diseases. Using multiple microarray datasets, this paper cross compares two different methods for classification and feature selection. Since individual gene count in microarray data are too many, most informative genes should be selected and used. For this selection, we have tried Relief and LASSO feature selection methods. After picking informative genes from microarray data, classification is performed with Support Vector Machines (SVM), Multilayer Perceptron Networks (MLP) and Random Forest (RF) methods which are widely used in multiple classification tasks. The overall accuracy and training time with LASSO and SVM outperforms most of the approaches proposed.

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Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi-Cover
  • ISSN: 1300-7688
  • Yayın Aralığı: Yılda 3 Sayı
  • Başlangıç: 1995
  • Yayıncı: Süleyman Demirel Üniversitesi