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Microarray data classification using automatic SVM kernel selection

journal contribution
posted on 06.12.2017, 00:00 by Jesmin Nahar, A B M Shawkat Ali, YP Chen
Microarray 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. Among the large scale of kernels, a significant research question requires answering what is the best kernel for patient diagnosis based on microarray data using SVM?. We explain three solutions based on data visualization and quantitative measure. The proposed solution is then tested by different types of microarray problems. Finally, we find that the rule based approach can be useful for automatic kernel selection of SVM to classify microarray data.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

26

Issue

10

Start Page

707

End Page

712

Number of Pages

6

ISSN

1044-5498

Location

New Rochelle, NY 10801-5215, USA

Publisher

Mary Ann Liebert, Inc., publishers

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Deakin University; Faculty of Business and Informatics;

Era Eligible

Yes

Journal

DNA and cell biology.

Exports

CQUniversity

Categories

Exports