Evolutionary Selection of Minimum Number of Features for Classification of Gene Expression Data Using Genetic Algorithms

Kucukural A., Yeniterzi R., Yeniterzi S., Sezerman O. U.

Annual Conference of Genetic and Evolutionary Computation Conference, London, Kanada, 7 - 11 Temmuz 2007, ss.401-406 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1145/1276958.1277040
  • Basıldığı Şehir: London
  • Basıldığı Ülke: Kanada
  • Sayfa Sayıları: ss.401-406
  • Anahtar Kelimeler: Biomarkers, colon cancer, prostate cancer, ovarian cancer, feature selection, classification, genetic algorithms, CANCER, PATTERNS
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır

Özet

Selecting the most relevant factors from genetic profiles that can optimally characterize Cellular states is of crucial importance in identifying complex disease genes and biomarkers for disease diagnosis and assessing drug efficiency. In this paper, we present an approach using a genetic algorithm for a feature subset selection problem that can he used in selecting the near optimum set of genes for classification of cancer data. In substantial improvement over existing methods, we classified cancer data with high accuracy with less features.