SMOTE Balancing

Generates a more balanced data set by creating synthetic instances of the minority class using the SMOTE algorithm. The algorithm samples for each minority instance a new data point based on the K nearest neighbors of that data point. It can only be applied to tasks with purely numeric features. See smotefamily::SMOTE for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSmote$new(id = "smote", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "smote".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc. Instead of a Task, a TaskClassif is used as input and output during training and prediction.

The output during training is the input Task with added synthetic rows for the minority class. The output during prediction is the unchanged input.

State

The $state is a named list with the $state elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• K :: numeric(1)
  The number of nearest neighbors used for sampling new values. See SMOTE().

• dup_size :: numeric
  Desired times of synthetic minority instances over the original number of majority instances. See SMOTE().

Internals

If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002). “SMOTE: Synthetic Minority Over-sampling Technique.” Journal of Artificial Intelligence Research, 16, 321–357. doi:10.1613/jair.953 .

See also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

# Create example task
data = smotefamily::sample_generator(1000, ratio = 0.80)
data$result = factor(data$result)
task = TaskClassif$new(id = "example", backend = data, target = "result")
task$data()
#>       result          X1         X2
#>       <fctr>       <num>      <num>
#>    1:      p 0.546145996 0.67961492
#>    2:      n 0.079991565 0.61547644
#>    3:      n 0.643280776 0.03632103
#>    4:      n 0.731377352 0.32976618
#>    5:      n 0.004454134 0.94679939
#>   ---                              
#>  996:      n 0.629311925 0.85093931
#>  997:      p 0.607156249 0.52193177
#>  998:      n 0.026633458 0.57191021
#>  999:      n 0.380717913 0.93177893
#> 1000:      n 0.430693496 0.74375332
table(task$data()$result)
#> 
#>   n   p 
#> 835 165 

# Generate synthetic data for minority class
pop = po("smote")
smotedata = pop$train(list(task))[[1]]$data()
table(smotedata$result)
#> 
#>   n   p 
#> 835 825