Abstract
In this paper we show how the survival analysis problem can be formulated in terms of support vector regression, starting from a quantile regression perspective. We define an appropriate weighted loss function which takes into account possibly censored observations, and we prove bounds on the estimation error and on the quantile property. We deduce that censoring is a limiting factor in the accuracy of solutions, though the overall rate of convergence is O(n − 1/2). Finally we show some applications of the model to synthetic data, and to the German Breast Cancer Study Group 2 data.
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Eleuteri, A., Taktak, A.F.G. (2012). Support Vector Machines for Survival Regression. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_15
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DOI: https://doi.org/10.1007/978-3-642-35686-5_15
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