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FSelector

R package FSelector: Selecting attributes. This package provides functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible.

Keywords for this software

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  • R package
  • feature selection
  • arXiv_stat.ML
  • filter methods
  • R
  • arXiv_publication
  • Machine Learning
  • arXiv_cs.LG
  • search algorithms
  • variable ranking
  • Turing machines
  • statistical algorithms
  • high-dimensional data
  • repetition
  • implementation in R
  • random forest
  • randomization
  • mRMRe model
  • benchmark
  • pre-processing
  • mutual information
  • scalability and parallelization
  • Journal of Statistical Software
  • feature ranking
  • iteration
  • Feature Selection
  • floating-point computations
  • verification
  • wrapper methods
  • precision of computations

  • URL: cran.r-project.org/web...
  • Code
  • InternetArchive
  • Manual: cran.r-project.org/web...
  • Authors: Piotr Romanski
  • Dependencies: R

  • Add information on this software.


  • Related software:
  • R
  • varSelRF
  • GeneSrF
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  • Boruta
  • rpart
  • mlr
  • mlbench
  • Kernlab
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  • Show more...
  • FSinR
  • ggmap
  • MXM
  • OpenML
  • R2WinBUGS
  • randomForest
  • adabag
  • spFSR
  • featurefinder
  • gaselect
  • Show less...

References in zbMATH (referenced in 5 articles )

Showing results 1 to 5 of 5.
y Sorted by year (citations)

  1. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  2. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  3. Gilles Kratzer, Reinhard Furrer: varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets (2018) arXiv
  4. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  5. Miron Kursa; Witold Rudnicki: Feature Selection with the Boruta Package (2010) not zbMATH

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    • Top MSC classes
      • 62 Statistics
      • 65 Numerical analysis
      • 68 Computer science

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