Comparison of different feature extraction methods on classification of gene expression data

Argunash A. O., Akan B., Ercil A., Sezerman U.

IEEE 15th Signal Processing and Communications Applications Conference, Eskişehir, Türkiye, 11 - 13 Haziran 2007, ss.921-923 identifier identifier

  • 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).