Table 1 Random forest feature selection methods and their permutation requirements
Permute | Method | P-value | Brief description |
|---|---|---|---|
No | binomialRF [15] | Yes | Optimal splitting features’ p-values obtained via one-sided correlated binomial tests |
EFS [16] | No | Calculates a global score for each feature using 8 different metrics to measure importance and selects features whose score exceeds the median global score | |
AUC-RF [17] | No | Iteratively trains a random forest algorithm and removes predictors in a stepwise fashion to maximize an AUC increase | |
RFE, dRFE [18] | No | Iteratively trains a random forest (RF) model and drops uninformative features based on a user-defined criterion | |
RF-ACE [19] | No | Creates phony variables called “Artificial Contrasts with Ensembles”, and compares how often these sham variables are used over the real ones | |
R2VIM [12] | No | Calculates variable importance (VI) and divides by minimum VI to create relative VI, and choose important features based on a pre-selected cutoff | |
VarSelRF, geneSrF [5] | No | Iteratively removes worst .20 (or x-percentage) of all features; retrains RF; selects smallest feature set within one set of best models | |
Yes | Vita [20] | Yes | P-values are calculated based on empirical null distribution of non-positive importance scores that accelerate null distribution estimates |
Perm [20] | Yes | Permutes outcomes (Y) and determines importance based on which features retained a larger importance in Yoriginal vs. Ypermuted | |
PIMP [14] | Yes | Permutes outcome and determines features’ priority based on increases in mutual information or Gini errors. A feature’s p-values is produced by an importance measure fitted to a distribution | |
VSURF [17] | No | Two-step FS algorithm: 1) uses predictor permutations to identify features robust to noise, and 2) refines model by conducting step-forward inclusion of features until error convergence | |
Boruta [13] | No | Creates phony predictors by permuting the values of the shadow vars. Runs RF to identify features’ Z-scores. Eliminates features whose Z-score are less than a threshold. Repeats until convergence |