  We consider the problem of costly feature classification, where we
sequentially select the subset of features to make a balance between the
classification error and the feature cost. In this paper, we first cast the
task into a MDP problem and use Advantage Actor Critic algorithm to solve it.
In order to further improve the agent's performance and make the policy
explainable, we employ the Monte Carlo Tree Search to update the policy
iteratively. During the procedure, we also consider its performance on the
unbalanced dataset and its sensitivity to the missing value. We evaluate our
model on multiple datasets and find it outperforms other methods.
