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Random Forest (RF) is a powerful supervised learner and has been popularlyused in many applications such as bioinformatics.  In this work we propose the guided random forest (GRF) for feature selection.Similar to a feature selection method called guided regularized random forest(GRRF), GRF is built using the importance scores from an ordinary RF. However,the trees in GRRF are built sequentially, are highly correlated and do notallow for parallel computing, while the trees in GRF are built independentlyand can be implemented in parallel. Experiments on 10 high-dimensional genedata sets show that, with a fixed parameter value (without tuning theparameter), RF applied to features selected by GRF outperforms RF applied toall features on 9 data sets and 7 of them have significant differences at the0.05 level. Therefore, both accuracy and interpretability are significantlyimproved. GRF selects more features than GRRF, however, leads to betterclassification accuracy. Note in this work the guided random forest is guidedby the importance scores from an ordinary random forest, however, it can alsobe guided by other methods such as human insights (by specifying $\lambda_i$).GRF can be used in "RRF" v1.4 (and later versions), a package that alsoincludes the regularized random forest methods.

Guided Random Forest in the RRF Package