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Survival support vector machine. Calls survivalsvm::survivalsvm() from survivalsvm.

Details

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.

makediff3 is recommended when using type = "hybrid".

Dictionary

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

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

Meta Information

  • Task type: “surv”

  • Predict Types: “crank”, “response”

  • Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalsvm

Parameters

IdTypeDefaultLevelsRange
typecharacterregressionregression, vanbelle1, vanbelle2, hybrid-
diff.methcharacter-makediff1, makediff2, makediff3-
gamma.muuntyped--
opt.methcharacterquadprogquadprog, ipop-
kernelcharacterlin_kernellin_kernel, add_kernel, rbf_kernel, poly_kernel-
kernel.parsuntyped--
sgf.svinteger5\([0, \infty)\)
sigfinteger7\([0, \infty)\)
maxiterinteger20\([0, \infty)\)
marginnumeric0.05\([0, \infty)\)
boundnumeric10\([0, \infty)\)
eig.tolnumeric1e-06\([0, \infty)\)
conv.tolnumeric1e-07\([0, \infty)\)
posd.tolnumeric1e-08\([0, \infty)\)

References

Van Belle, Vanya, Pelckmans, Kristiaan, Van Huffel, Sabine, Suykens, AK J (2011). “Improved performance on high-dimensional survival data by application of Survival-SVM.” Bioinformatics, 27(1), 87--94.

Van Belle, Vanya, Pelckmans, Kristiaan, Van Huffel, Sabine, Suykens, AK J (2011). “Support vector methods for survival analysis: a comparison between ranking and regression approaches.” Artificial intelligence in medicine, 53(2), 107--118.

Shivaswamy, K P, Chu, Wei, Jansche, Martin (2007). “A support vector approach to censored targets.” In Seventh IEEE international conference on data mining (ICDM 2007), 655--660. IEEE.

See also

Author

RaphaelS1

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.

Examples

learner = mlr3::lrn("surv.svm")
print(learner)
#> <LearnerSurvSVM:surv.svm>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, survivalsvm
#> * Predict Type: crank
#> * Feature types: integer, numeric, character, factor, logical
#> * Properties: -

# available parameters:
learner$param_set$ids()
#>  [1] "type"        "diff.meth"   "gamma.mu"    "opt.meth"    "kernel"     
#>  [6] "kernel.pars" "sgf.sv"      "sigf"        "maxiter"     "margin"     
#> [11] "bound"       "eig.tol"     "conv.tol"    "posd.tol"