Gene classification using appropriate feature selection method and Fukunaga-Koontz Transform kernel Uygun öznitelik seçimi ve Fukunaga-Koontz dönü şümü çekirdeǧi yöntemi ile gen siniflandirilmasi

Dinç S., Ayan U., Bal A.

2010 7th National Conference on Electrical, Electronics and Computer Engineering, ELECO 2010, Bursa, Türkiye, 2 - 05 Aralık 2010, ss.539-543 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.539-543
  • İstanbul Kültür Üniversitesi Adresli: Evet

Özet

In this paper, a new algorithm related with feature selection method mostly used in data mining, machine learning and pattern recognition areas is proposed. Classical Fukunaga-Koontz Transform is extended to a binary kernel classifier. We used cDNA microarrays to assess 11.000 gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute and Diffuse large B-cell lymphoma data including 62 cells and more than 4.000 genes. Proposed two stage algorithm applied on NCI60 and LYM dataset is compared with other feature selection models in details.