Feature Selection with Genetic Search

Feature selection using the Genetic Algorithm from the package genalg.

Dictionary

This FSelector can be instantiated with the associated sugar function fs():

fs("genetic_search")

Control Parameters

For the meaning of the control parameters, see genalg::rbga.bin(). genalg::rbga.bin() internally terminates after iters iteration. We set ìters = 100000 to allow the termination via our terminators. If more iterations are needed, set ìters to a higher value in the parameter set.

See also

Other FSelector: FSelector, mlr_fselectors, mlr_fselectors_design_points, mlr_fselectors_exhaustive_search, mlr_fselectors_random_search, mlr_fselectors_rfe, mlr_fselectors_rfecv, mlr_fselectors_sequential, mlr_fselectors_shadow_variable_search

Super classes

mlr3fselect::FSelector -> mlr3fselect::FSelectorBatch -> FSelectorBatchGeneticSearch

Methods

Public methods

• FSelectorBatchGeneticSearch$new()

• FSelectorBatchGeneticSearch$clone()

Inherited methods

• mlr3fselect::FSelector$format()
• mlr3fselect::FSelector$help()
• mlr3fselect::FSelector$print()
• mlr3fselect::FSelectorBatch$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

FSelectorBatchGeneticSearch$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorBatchGeneticSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Feature Selection
# \donttest{

# retrieve task and load learner
task = tsk("penguins")
learner = lrn("classif.rpart")

# run feature selection on the Palmer Penguins data set
instance = fselect(
  fselector = fs("genetic_search"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10
)

# best performing feature set
instance$result
#>    bill_depth bill_length body_mass flipper_length island    sex   year
#>        <lgcl>      <lgcl>    <lgcl>         <lgcl> <lgcl> <lgcl> <lgcl>
#> 1:      FALSE       FALSE     FALSE           TRUE  FALSE  FALSE  FALSE
#>          features n_features classif.ce
#>            <list>      <int>      <num>
#> 1: flipper_length          1  0.1913043

# all evaluated feature sets
as.data.table(instance$archive)
#>     bill_depth bill_length body_mass flipper_length island    sex   year
#>         <lgcl>      <lgcl>    <lgcl>         <lgcl> <lgcl> <lgcl> <lgcl>
#>  1:      FALSE       FALSE     FALSE          FALSE  FALSE   TRUE  FALSE
#>  2:      FALSE       FALSE     FALSE          FALSE  FALSE  FALSE   TRUE
#>  3:      FALSE       FALSE     FALSE          FALSE  FALSE  FALSE   TRUE
#>  4:      FALSE       FALSE     FALSE          FALSE  FALSE   TRUE  FALSE
#>  5:      FALSE       FALSE     FALSE          FALSE   TRUE  FALSE  FALSE
#>  6:      FALSE        TRUE     FALSE          FALSE  FALSE  FALSE  FALSE
#>  7:      FALSE        TRUE     FALSE          FALSE  FALSE  FALSE  FALSE
#>  8:      FALSE       FALSE     FALSE          FALSE   TRUE  FALSE  FALSE
#>  9:       TRUE       FALSE     FALSE          FALSE   TRUE  FALSE  FALSE
#> 10:      FALSE       FALSE     FALSE           TRUE  FALSE  FALSE  FALSE
#>     classif.ce runtime_learners           timestamp batch_nr warnings errors
#>          <num>            <num>              <POSc>    <int>    <int>  <int>
#>  1:  0.6000000            0.004 2025-02-12 16:49:10        1        0      0
#>  2:  0.6000000            0.004 2025-02-12 16:49:10        2        0      0
#>  3:  0.6000000            0.005 2025-02-12 16:49:10        3        0      0
#>  4:  0.6000000            0.004 2025-02-12 16:49:10        4        0      0
#>  5:  0.2347826            0.005 2025-02-12 16:49:10        5        0      0
#>  6:  0.2521739            0.005 2025-02-12 16:49:10        6        0      0
#>  7:  0.2521739            0.005 2025-02-12 16:49:10        7        0      0
#>  8:  0.2347826            0.004 2025-02-12 16:49:10        8        0      0
#>  9:  0.2260870            0.005 2025-02-12 16:49:10        9        0      0
#> 10:  0.1913043            0.004 2025-02-12 16:49:10       10        0      0
#>              features n_features  resample_result
#>                <list>     <list>           <list>
#>  1:               sex          1 <ResampleResult>
#>  2:              year          1 <ResampleResult>
#>  3:              year          1 <ResampleResult>
#>  4:               sex          1 <ResampleResult>
#>  5:            island          1 <ResampleResult>
#>  6:       bill_length          1 <ResampleResult>
#>  7:       bill_length          1 <ResampleResult>
#>  8:            island          1 <ResampleResult>
#>  9: bill_depth,island          2 <ResampleResult>
#> 10:    flipper_length          1 <ResampleResult>

# subset the task and fit the final model
task$select(instance$result_feature_set)
learner$train(task)
# }