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Microarray data classification using automatic SVM kernel selection

Nahar, Jesmin, Ali, Shawkat and Chen, Yi-Ping Phoebe 2007, Microarray data classification using automatic SVM kernel selection, DNA and cell biology, vol. 26, no. 10, pp. 707-712.

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Title Microarray data classification using automatic SVM kernel selection
Author(s) Nahar, Jesmin
Ali, Shawkat
Chen, Yi-Ping Phoebe
Journal name DNA and cell biology
Volume number 26
Issue number 10
Start page 707
End page 712
Publisher Mary Ann Liebert, Inc.
Place of publication New Rochelle, N. Y.
Publication date 2007-10
ISSN 1044-5498
Summary Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Mary Ann Liebert Publishers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007572

Document type: Journal Article
Collections: School of Engineering and Information Technology
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