Mahmoudian M, Venäläinen M, Klèn R, Elo L (2021). “Stable Iterative Variable Selection.” Bioinformatics. ISSN 1367-4803, doi: 10.1093/bioinformatics/btab501, R package is available via https://cran.r-project.org/package=sivs, https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab501/39070854/btab501.pdf, https://doi.org/10.1093/bioinformatics/btab501.

Mahmoudian M, Venäläinen M, Klèn R, Elo L (2021). sivs: Stable Iterative Variable Selection. R package version 0.2.5, https://CRAN.R-project.org/package=sivs.

Corresponding BibTeX entries:

  @Article{,
    title = {Stable Iterative Variable Selection},
    author = {Mehrad Mahmoudian and Mikko Venäläinen and Riku Klèn and
      Laura Elo},
    journal = {Bioinformatics},
    year = {2021},
    month = {7},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btab501},
    url = {https://doi.org/10.1093/bioinformatics/btab501},
    note = {R package is available via
      https://cran.r-project.org/package=sivs},
    eprint =
      {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab501/39070854/btab501.pdf},
    abstract = {The emergence of datasets with tens of thousands of
      features, such as high-throughput omics biomedical data,
      highlights the importance of reducing the feature space into a
      distilled subset that can truly capture the signal for research
      and industry by aiding in finding more effective biomarkers for
      the question in hand. A good feature set also facilitates
      building robust predictive models with improved interpretability
      and convergence of the applied method due to the smaller feature
      space.Here, we present a robust feature selection method named
      Stable Iterative Variable Selection (SIVS) and assess its
      performance over both omics and clinical data types. As a
      performance assessment metric, we compared the number and
      goodness of the selected feature using SIVS to those selected by
      LASSO regression. The results suggested that the feature space
      selected by SIVS was, on average, 41\% smaller, without having a
      negative effect on the model performance. A similar result was
      observed for comparison with Boruta and Caret RFE.The method is
      implemented as an R package under GNU General Public License v3.0
      and is accessible via Comprehensive R Archive Network (CRAN) via
      https://cran.r-project.org/web/packages/sivs/index.html or
      through Github via
      https://github.com/mmahmoudian/sivs/Supplementary data are
      available at Bioinformatics online.},
  }

  @Manual{,
    title = {{sivs}: Stable Iterative Variable Selection},
    author = {Mehrad Mahmoudian and Mikko Venäläinen and Riku Klèn and
      Laura Elo},
    year = {2021},
    note = {R package version 0.2.5},
    url = {https://CRAN.R-project.org/package=sivs},
  }