mlr_filters_disr: Double Input Symmetrical Relevance Filter

In mlr-org/mlr3featsel: Filter Based Feature Selection for 'mlr3'

Double Input Symmetrical Relevance Filter

Description

Double input symmetrical relevance filter calling praznik::DISR() from package praznik.

This filter supports partial scoring (see Filter).

Details

As the scores calculated by the praznik package are not monotone due to the greedy forward fashion, the returned scores simply reflect the selection order: 1, (k-1)/k, ..., 1/k where k is the number of selected features.

Threading is disabled by default (hyperparameter threads is set to 1). Set to a number ⁠>= 2⁠ to enable threading, or to 0 for auto-detecting the number of available cores.

Super class

mlr3filters::Filter -> FilterDISR

Methods

Public methods

• FilterDISR$new()

• FilterDISR$clone()

Inherited methods

• mlr3filters::Filter$calculate()
• mlr3filters::Filter$format()
• mlr3filters::Filter$help()
• mlr3filters::Filter$print()

---

Method new()

Create a FilterDISR object.

Usage

FilterDISR$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterDISR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Kursa MB (2021). “Praznik: High performance information-based feature selection.” SoftwareX, 16, 100819. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.softx.2021.100819")}.

For a benchmark of filter methods:

Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020). “Benchmark for filter methods for feature selection in high-dimensional classification data.” Computational Statistics & Data Analysis, 143, 106839. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2019.106839")}.

See Also

• PipeOpFilter for filter-based feature selection.

• Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters, mlr_filters_anova, mlr_filters_auc, mlr_filters_boruta, mlr_filters_carscore, mlr_filters_carsurvscore, mlr_filters_cmim, mlr_filters_correlation, mlr_filters_find_correlation, mlr_filters_importance, mlr_filters_information_gain, mlr_filters_jmi, mlr_filters_jmim, mlr_filters_kruskal_test, mlr_filters_mim, mlr_filters_mrmr, mlr_filters_njmim, mlr_filters_performance, mlr_filters_permutation, mlr_filters_relief, mlr_filters_selected_features, mlr_filters_univariate_cox, mlr_filters_variance

Examples

if (requireNamespace("praznik")) {
  task = mlr3::tsk("iris")
  filter = flt("disr")
  filter$calculate(task)
  as.data.table(filter)
}

if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart", "praznik"), quietly = TRUE)) {
  library("mlr3pipelines")
  task = mlr3::tsk("spam")

  # Note: `filter.frac` is selected randomly and should be tuned.

  graph = po("filter", filter = flt("disr"), filter.frac = 0.5) %>>%
    po("learner", mlr3::lrn("classif.rpart"))

  graph$train(task)
}

mlr-org/mlr3featsel documentation built on April 14, 2024, 12:17 p.m.