Random Forest Variable Importance-based Selection Algorithm in Class
  Imbalance Problem
Yunbi Nam Sunwoo Han
Random Forest is a machine learning method that offers many advantages,
including the ability to easily measure variable importance. Class balancing
technique is a well-known solution to deal with class imbalance problem.
However, it has not been actively studied on RF variable importance. In this
paper, we study the effect of class balancing on RF variable importance. Our
simulation results show that over-sampling is effective in correctly measuring
variable importance in class imbalanced situations with small sample size,
while under-sampling fails to differentiate important and non-informative
variables. We then propose a variable selection algorithm that utilizes RF
variable importance and its confidence interval. Through an experimental study
using many real and artificial datasets, we demonstrate that our proposed
algorithm efficiently selects an optimal feature set, leading to improved
prediction performance in class imbalance problem.