R package Boruta: A wrapper algorithm for all-relevant feature selection. Boruta is an all-relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes’ importance with importance achievable at random, estimated using their permuted copies.

References in zbMATH (referenced in 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  3. Mustaqeem, Anam; Anwar, Syed Muhammad; Majid, Muahammad: Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants (2018)
  4. Thomas, Janek; Hepp, Tobias; Mayr, Andreas; Bischl, Bernd: Probing for sparse and fast variable selection with model-based boosting (2017)
  5. Ma, Yuting; Zheng, Tian: Boosted sparse nonlinear distance metric learning (2016)
  6. Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Thi: Two-level quantile regression forests for bias correction in range prediction (2015)
  7. Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot: VSURF: An R Package for Variable Selection Using Random Forests (2015) not zbMATH
  8. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  9. Deng, Houtao; Runger, George: Gene selection with guided regularized random forest (2013) ioport
  10. Barb, Adrian S.: Gaussian mixture models for semantic ranking in domain specific databases with application in radiology (2011) ioport
  11. Kursa, Miron B.; Jankowski, Aleksander; Rudnicki, Witold R.: Boruta -- a system for feature selection (2010) ioport
  12. Miron Kursa; Witold Rudnicki: Feature Selection with the Boruta Package (2010) not zbMATH