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Support Vector Machine
Putri W Novianti
Victor L Jong
Biostatistics & Research Support
Julius Center for Health Sciences and Primary Care
University Medical Center Utrecht
• Binary classification method
• The method finds the best decision hyperplane that separate sample from
two classes with maximum margin
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What if the problem is not linearly separable?
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- SVM only handle binary classification
- Although binary classification is the most common classification in microarray, multiclass outcome could be occur in practice
- Modification is needed to handle multiclass outcome
- one-versus-rest (OVR)
- one-versus-one (OVO)
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Multiclass outcome
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Multiclass outcome
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OVR-SVM
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OVO-SVM
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Example 1. Classification in Iris Data
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Example 1. Classification in Iris Data
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SVM for Regression
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SVM for Regression
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References
[1] Zhang, X. Support Vector Machine. Lecture slides on Data Mining course. Fall 2010, KSA: KAUST
[2] Statnikov, A. et al. 2005. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics, 21:5, 631-643
[3] Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning, second edition. 2009. New York: Springer
[4] Guyon, I et al. 2002. Gene selection for cancer classification using support vector machines. Machine Learning, 49, 389-422
[5] Meyer, D. et al. 2012. R package: e1071.
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