Survival SVM: a practical scalable algorithm

This work advances the Support Vector Machine (SVM) based approach for predictive modelling of failure time data as proposed in [1]. The main results concern a drastic reduction in computation time, an improved criterion for model selection, and the use of additive models for improved interpretability in this context. Particular attention is given towards the influence of right-censoring in the methods. The approach is illustrated on a case-study in prostate cancer.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
• Keywords: ESANN 2008, Failure Time Data, Improved Criterion, Neural Networks, Support Vector Machine