Calls survivalsvm::survivalsvm().

Four possible SVMs can be implemented, dependent on the type parameter. These correspond to predicting the survival time via regression (regression), predicting a continuous rank (vanbelle1, vanbelle2), or a hybrid of the two (hybrid). Whichever type is chosen determines how the crank predict type is calculated, but in any case all can be considered a valid continuous ranking.

To be in line with the Van Belle papers and to prevent the learner crashing without user-set parameters, the default diff.meth is set to diffmeth3 with gamma.mu equal to 0.1.

Format

R6::R6Class() inheriting from LearnerSurv.

Construction

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

Meta Information

  • Type: "surv"

  • Predict Types: crank

  • Feature Types: integer, numeric

  • Packages: survivalsvm

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.

See also

Examples

library(mlr3) task = tgen("simsurv")$generate(200) learner = lrn("surv.svm") resampling = rsmp("cv", folds = 3) resample(task, learner, resampling)
#> <ResampleResult> of 3 iterations #> * Task: simsurv #> * Learner: surv.svm #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations