Motivation Random forests are fast, flexible and represent a robust approach to analyze high
dimensional data. A key advantage over alternative machine learning algorithms are …

Background Variable importance measures for random forests have been receiving
increased attention as a means of variable selection in many classification tasks in …

Background Random forest based variable importance measures have become popular
tools for assessing the contributions of the predictor variables in a fitted random forest. In this …

Motivation Random forest is a popular machine learning approach for the analysis of high-
dimensional data because it is flexible and provides variable importance measures for the …

The default variable-importance measure in random forests, Gini importance, has been
shown to suffer from the bias of the underlying Gini-gain splitting criterion. While the …

Background Random forests are becoming increasingly popular in many scientific fields
because they can cope with" small n large p" problems, complex interactions and even …

Background The random forest (RF) method is a commonly used tool for classification with
high dimensional data as well as for ranking candidate predictors based on the so-called …

Random forests are a commonly used tool for classification and for ranking candidate
predictors based on the so-called variable importance measures. These measures attribute …

Background Random forests (RF) have been increasingly used in applications such as
genome-wide association and microarray studies where predictor correlation is frequently …

Tree ensemble methods such as random forests (Mach. Learn. 45 (2001) 5–32) are very
popular to handle high-dimensional tabular data sets, notably because of their ability to …