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.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

  • Type: "surv"

  • Predict Types: crank

  • Feature Types: integer, numeric

  • Properties: -

  • Packages: survivalsvm

References

Belle VV, Pelckmans K, Huffel SV, Suykens JAK (2010). “Improved performance on high-dimensional survival data by application of Survival-SVM.” Bioinformatics, 27(1), 87--94. doi: 10.1093/bioinformatics/btq617 .

Belle VV, Pelckmans K, Huffel SV, Suykens JA (2011). “Support vector methods for survival analysis: a comparison between ranking and regression approaches.” Artificial Intelligence in Medicine, 53(2), 107--118. doi: 10.1016/j.artmed.2011.06.006 .

See also

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvSVM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvSVM$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.