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Microarray data classification using automatic SVM kernel selection
journal contribution
posted on 2017-12-06, 00:00 authored by Jesmin Nahar, A B M Shawkat Ali, YP ChenMicroarray 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
26Issue
10Start Page
707End Page
712Number of Pages
6ISSN
1044-5498Location
New Rochelle, NY 10801-5215, USAPublisher
Mary Ann Liebert, Inc., publishersLanguage
en-ausPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Deakin University; Faculty of Business and Informatics;Era Eligible
- Yes