Semi-supervised SVM-based feature selection for cancer classification using microarray gene expression data
Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/59470/ http://dx.doi.org/10.1007/978-3-319-19066-2_45 |
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| Institution: | Universiti Teknologi Malaysia |