Lojistik Regresyonun Özellik Azaltma Teknikleri ile Gen Dizilimlerinin Sınıflandırılmasındaki Başarısı

Öz

Gen dizilimlerinin sınıflandırılması, hastalıkların ön görülebilmesi veya teşhis edilebilmesinde çok önemli rol oynamaktadır. Bütün gen dizilimi üzerinde etkili bir sınıflandırma yapabilmek mümkün olmadığından sağlıklı bir sınıflandırma yapılabilmesi için gerekli bilgiyi içeren genlerin (özelliklerin) özellik azaltma algoritmaları ile ayıklanması önem taşımaktadır. Bu çalışmada, özellikleri azaltmak için sezgisel arama teknikleri, özellik azaltma yaklaşımları(filter, wrapper, vb.) gibi farklı yöntemler analiz edilerek ön işleme adımının daha etkin bir şekilde gerçekleştirilmesi; bunun sonucunda elde edilen veri kümelerinin LR (Lojistik Regresyon) ve SVM (Destek Vektör Makineleri) gibi güçlü sınıflandırma araçları ile daha etkin şekilde sınıflandırılması hedeflenmiştir. Makine öğrenmesinde güçlü bir sınıflandırıcı olarak kabul edilen LR sınıflandırıcısı, özellik eksiltme yöntemleri ile gen dizilimlerinin sınıflandırılmasında SVM kadar geçerli ve etkin sınıflama aracı haline gelmiştir.

Anahtar Kelimeler

• Gen analizi, makine öğrenmesi, lojistik regresyon, özellik azaltma, SVM

The Success Of Logistic Regression With Feature Reduction Techniques On Microarray Gene Classification

Öz

DNA microarray classification is important to discovery of differentially expressed genes between normal and diseased patients are a central research problem in bioinformatics. All the genes used in the expression profile are not informative. Further, many of them are redundant. A pre-processing step in order to reduce the number of genes by feature selection and still retaining best class prediction accuracy for the cla1 ssifier is crucial for precise tumor classification. In this study comparison between class prediction accuracy of two different classifiers, LR (Logistic Regression) and SVM (Support Vector Machines), was carried out using the best genes select by wrapper and filter technique to use heuristic search methods. We conclude that LR together with heuristic search based feature selection is the as efficient as SVM to the microarray gene prediction techniques.

Anahtar Kelimeler

• Microarray analysis, binary classification, machine learning, logistic regression, feature reduction, tumor analysis, SVM

Kaynakça

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