SMOTENC Balancing

Generates a more balanced data set by creating synthetic instances of the minority class for nominal and continuous data using the SMOTENC algorithm.

The algorithm generates for each minority instance a new data point based on the k nearest neighbors of that data point. It treats integer features as numeric. To not change feature types, the numeric, synthetic data generated for these features are rounded back to integer. Because of this, data generated through usage of this PipeOp is not exactly equal to data generated by calling themis::smotenc directly on the same data set.

It can only be applied to classification tasks with factor (or ordered) features and at least one numeric (or integer) feature that have no missing values.

See themis::smotenc for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSmoteNC$new(id = "smotenc", param_vals = list())

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

• 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.

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 :: integer(1)
  Number of nearest neighbors used for generating new values from the minority class. Default is 5.

• over_ratio :: numeric(1)
  Ratio of the majority to minority class. Default is 1. For details, see themis::smotenc.

Fields

Only fields inherited from PipeOpTaskPreproc/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, 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_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_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 = 200, replace = TRUE, prob = c(0.1, 0.9))),
  feature = rnorm(200)
)
task = TaskClassif$new(id = "example", backend = data, target = "target")
task$head()
#>    target    feature
#>    <fctr>      <num>
#> 1:     c2 -1.0084861
#> 2:     c2 -0.2084793
#> 3:     c2 -0.2704835
#> 4:     c2 -0.7940955
#> 5:     c1 -0.9476550
#> 6:     c2 -1.5796010
table(task$data(cols = "target"))
#> target
#>  c1  c2 
#>  28 172 

# Generate synthetic data for minority class
pop = po("smotenc")
smotenc_result = pop$train(list(task))[[1]]$data()
nrow(smotenc_result)
#> [1] 344
table(smotenc_result$target)
#> 
#>  c1  c2 
#> 172 172