ADAS Balancing

Generates a more balanced data set by creating synthetic instances of the minority classes using the ADASYN algorithm.

The algorithm generates for each minority instance new data points based on its K nearest neighbors and the difficulty of learning for that data point. It can only be applied to tasks with numeric features that have no missing values.

See smotefamily::ADAS for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpADAS$new(id = "adas", param_vals = list())

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

• 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. Default is 5. See ADAS().

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

He H, Bai Y, Garcia, A. E, Li S (2008). “ADASYN: Adaptive synthetic sampling approach for imbalanced learning.” In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322-1328. doi:10.1109/IJCNN.2008.4633969 .

See also

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

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, 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_smote, 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 = data.frame(
  target = factor(sample(c("c1", "c2"), size = 300, replace = TRUE, prob = c(0.1, 0.9))),
  x1 = rnorm(300),
  x2 = rnorm(300)
)
task = TaskClassif$new(id = "example", backend = data, target = "target")
task$head()
#>    target         x1         x2
#>    <fctr>      <num>      <num>
#> 1:     c2 -0.3002703  0.1054926
#> 2:     c2 -0.3084548  0.8072830
#> 3:     c2  0.5561903 -0.1168897
#> 4:     c2  0.6400171 -1.4728225
#> 5:     c1 -1.8017986  0.4839497
#> 6:     c2 -0.7361059  0.1665491
table(task$data(cols = "target"))
#> target
#>  c1  c2 
#>  26 274 

# Generate synthetic data for minority class
pop = po("adas")
adas_result = pop$train(list(task))[[1]]$data()
nrow(adas_result)
#> [1] 546
table(adas_result$target)
#> 
#>  c1  c2 
#> 272 274