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Gene classification using appropriate feature selection method and Fukunaga-Koontz Transform kernel

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3 Author(s)
Dinç, S. ; Kontrol ve Otomasyon Muhendisligi Bolumu, Yildiz Teknik Univ., Istanbul, Turkey ; Ayan, U. ; Bal, A.

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.

Published in:

Electrical, Electronics and Computer Engineering (ELECO), 2010 National Conference on

Date of Conference:

2-5 Dec. 2010