BIOINFORMATICS ORIGINAL PAPER

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance (2020, background): “…(oob) (Breiman, 2001) importance scores are one such popular measure, though many issues such as a preference for correlated features and those with many categories have been noted in the years following their introduction (Strobl et al., 2007; Nicodemus et al., 2010; Tolo¸si and Lengauer, 2011).”
Please Stop Permuting Features: An Explanation and Alternatives (2019, result): “Similar critiques can be found in Strobl et al. (2007, 2008) and Toloşi and Lengauer (2011).”
A random forest guided tour (2015, methodology): “Similarly, the Reinforcement Learning Trees (RLT) of Zhu et al. (2015) build at each node a random forest to determine the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split.”
Correlation and variable importance in random forests (2013, methodology): “In the context of random forests, the impact of correlated predictors on variable selection methods has been highlighted by several simulation studies, see for instance Toloşi and Lengauer [34].”