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Joint mutual information filter calling praznik::JMI() in 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.

References

Kursa MB (2021). “Praznik: High performance information-based feature selection.” SoftwareX, 16, 100819. 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. doi:10.1016/j.csda.2019.106839 .

Super class

mlr3filters::Filter -> FilterJMI

Methods

Inherited methods


Method new()

Create a FilterJMI object.

Usage

FilterJMI$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

FilterJMI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("praznik")) {
  task = mlr3::tsk("iris")
  filter = flt("jmi")
  filter$calculate(task, nfeat = 2)
  as.data.table(filter)
}
#>         feature score
#> 1:  Petal.Width     1
#> 2: Sepal.Length     0
#> 3:  Sepal.Width    NA
#> 4: Petal.Length    NA

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

  graph$train(task)
}
#> $classif.rpart.output
#> NULL
#>