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")

Traits

  • Packages: survivalsvm

  • Predict Types: crank, response

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

  • Properties:

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.

Shivaswamy, P. K., Chu, W., & Jansche, M. (2007). A support vector approach to censored targets. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 655–660). https://doi.org/10.1109/ICDM.2007.93

See also

Author

RaphaelS1

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.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.svm")) print(learner)
#> <LearnerSurvSVM:surv.svm> #> * Model: - #> * Parameters: list() #> * Packages: 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"