Costly Features Classification using Monte Carlo Tree Search
Stony Brook University（石溪大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
