`R/LearnerSurvSVM.R`

`LearnerSurvSVM.Rd`

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

from package survivalsvm.

`R6::R6Class()`

inheriting from LearnerSurv.

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.

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

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) 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 #> * Performance: 0.480 [surv.harrells_c] #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations