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

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 profil...

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Bibliographic Details
Main Authors: Dinç, Semih, Ayan, Uğur, Bal, A
Format: Conference Proceeding
Language:English
Subjects:
Cancer
Classification algorithms
Gene expression
Kernel
Machine learning
Proteins
Transforms
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Summary: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.