  Random Forest (RF) is a powerful supervised learner and has been popularly
used 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 not
allow for parallel computing, while the trees in GRF are built independently
and can be implemented in parallel. Experiments on 10 high-dimensional gene
data sets show that, with a fixed parameter value (without tuning the
parameter), RF applied to features selected by GRF outperforms RF applied to
all features on 9 data sets and 7 of them have significant differences at the
0.05 level. Therefore, both accuracy and interpretability are significantly
improved. GRF selects more features than GRRF, however, leads to better
classification accuracy. Note in this work the guided random forest is guided
by the importance scores from an ordinary random forest, however, it can also
be 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 also
includes the regularized random forest methods.
