Evaluation of variable selection methods for random forests and omics data sets

By F. Degenhardt, S. Seifert, Silke Szymczak. Briefings Bioinform., 2017

625 citations recorded by the source corpus.

Abstract Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ff27b57405ca3ff53e3d17a6773fd4da441ba41a

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Citations in the corpus

The revival of the Gini importance? (2018, methodology): “Alternatively, the user may decide to build a new RF using the p-values as sampling weights for the variables, in a similar fashion as in "Guided RF" (Deng, 2013). Further, based on our results on the GWAS data, we recommend to increase the number of trees when computing variable importance measures”
A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling (2019, methodology): “Consistent with findings from Sanchez-Pinto (2018), our study found that VSURF had slightly better prediction error compared to Boruta and RRF. Degenhardt (2017) focused on omics datasets which were quite large, but concluded that Boruta and Altmann were suitable for low dimensional data; however,…”
A review on longitudinal data analysis with random forest (2022, methodology): “Thus, for high-dimensional dataset such as omics data, variable selection procedures based on variable importance measure are possible (see [23] and the reference therein for a description and comparison of various variable selection procedures based on variable importance measure).”