GeneSrF: gene selection with random forests (v. 20070524). GeneSrF is a web tool for gene selection in classification problems that uses random forest. Two approaches for gene selection are used: one is targeted towards identifying small, non-redundant sets of genes that have good predictive performance. The second is a more heuristic graphical approach that can be used to identify large sets of genes (including redundant genes) related to the outcome of interest. The first approach is described in detail in this paper. The R code is available as an R package from CRAN or from this link. For further details see the help.

References in zbMATH (referenced in 70 articles )

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  1. Guo, Liang; Liu, Jianya; Lu, Ruodan: Subsampling bias and the best-discrepancy systematic cross validation (2021)
  2. Salles, Thiago; Rocha, Leonardo; Gonçalves, Marcos: A bias-variance analysis of state-of-the-art random forest text classifiers (2021)
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  5. Mišić, Velibor V.: Optimization of tree ensembles (2020)
  6. Zhong, Guo; Pun, Chi-Man: Nonnegative self-representation with a fixed rank constraint for subspace clustering (2020)
  7. Ayyad, Sarah M.; Saleh, Ahmed I.; Labib, Labib M.: A new distributed feature selection technique for classifying gene expression data (2019)
  8. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  9. El Haouij, Neska; Poggi, Jean-Michel; Ghozi, Raja; Sevestre-Ghalila, Sylvie; Jaïdane, Mériem: Random forest-based approach for physiological functional variable selection for driver’s stress level classification (2019)
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  11. Kim, Ilmun; Lee, Ann B.; Lei, Jing: Global and local two-sample tests via regression (2019)
  12. Wang, Ling; Zhou, Dongfang; Tian, Hui; Zhang, Hao; Zhang, Wei: Parametric fault diagnosis of analog circuits based on a semi-supervised algorithm (2019)
  13. Yu, Xinghao; Xiao, Lishun; Zeng, Ping; Huang, Shuiping: Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information (2019)
  14. Duroux, Roxane; Scornet, Erwan: Impact of subsampling and tree depth on random forests (2018)
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  16. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  17. Janitza, Silke; Celik, Ender; Boulesteix, Anne-Laure: A computationally fast variable importance test for random forests for high-dimensional data (2018)
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