Support vector machines predicting either survival time, relative ranks or hybrid of risks. Calls survivalsvm::survivalsvm() from package survivalsvm.

## Format

R6::R6Class() inheriting from LearnerSurv.

## Details

Four possible models can be implemented, dependent on the model parameter. These correspond to predicting the prognosting index (PI) via regression, regression; predicting the PI by imposing a ranking constrant, vanbelle1, vanbelle2; a hybrid of the two hybrid. Whilst regression is the default, hybrid may be more efficient in tuning as it can be reduced to the previous depending on optimal parameters.

## Construction

LearnerSurvSVM$new() mlr_learners$get("surv.svm")
lrn("surv.svm")


## References

Van Belle, V., Pelcmans, K., Van Huffel S. and Suykens J. A.K. (2011a). Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinformatics, 27, 87-94.

Van Belle, V., Pelcmans, K., Van Huffel S. and Suykens J. A.K. (2011b). Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artificial Intelligence in Medicine, 53, 107-118.

library(mlr3)
#> * Errors: 0 in 0 iterations