Costly Features Classification using Monte Carlo Tree Search
This work addresses feature selection in classification for scenarios with cost constraints, but it appears incremental as it builds on existing reinforcement learning and search techniques.
The paper tackles the problem of costly feature classification by balancing classification error and feature cost, using a combination of Advantage Actor Critic and Monte Carlo Tree Search to improve performance and explainability, and reports that it outperforms other methods on multiple datasets.
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.