Comparison of different feature extraction methods on classification of gene expression data
IEEE 15th Signal Processing and Communications Applications Conference, Eskişehir, Türkiye, 11 - 13 Haziran 2007, ss.921-923, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu.2007.4298706
- Basıldığı Şehir: Eskişehir
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.921-923
- Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır
Özet
It is important to extract the most relevant features of the genetic profiles to determine the health condition of the cellular structure. Early diagnosis of the illnesses has a great importance in the treatment. In this study, we analyzed a gene expression data by classifying using Support Vector Machines after applying different feature extraction methods as Principal Component Analysis (PCA) and Independent Component Analysis [2] (ICA). Results have been compared with the results of the feature extraction algorithm based on Genetic Algorithm (GA).