Abstract
Support vector machine (SVM) is a popular method for classification, but there are few methods that utilize SVM for survival analysis in the literature because of the computational complexity. In this paper, we develop a novel \( {L_1} \) penalized SVM method for mining right-censored survival data (\( {L_1} \) SVMSURV). Our proposed method can simultaneously identify survival-associated prognostic factors and predict survival outcomes. It is easy to understand and efficient to use especially when applied to large datasets. Our method has been examined through both simulation and real data, and its performance is very good with limited experiments.
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Acknowledgment
This work was partially supported by NIH Grant 1R03CA133899-01A210 and NSF CCF-0729080.
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Liu, Z., Chen, D., Tian, G., Tang, ML., Tan, M., Sheng, L. (2010). Efficient Support Vector Machine Method for Survival Prediction with SEER Data. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_2
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_2
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