# Costly Features Classification using Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/2102.07073 · HTML: https://mlsanity.com/paper/2102.07073/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU](https://mlsanity.com/paper/1611.06256/) (2016)
- [Classification with Costly Features using Deep Reinforcement Learning](https://mlsanity.com/paper/1711.07364/) (2017)
- [Efficient Machine Learning for Big Data: A Review](https://mlsanity.com/paper/1503.05296/) (2015)
- [Adaptive Classification for Prediction Under a Budget](https://mlsanity.com/paper/1705.10194/) (2017)
- [Pruning Random Forests for Prediction on a Budget](https://mlsanity.com/paper/1606.05060/) (2016)
- [Coloring Big Graphs with AlphaGoZero](https://mlsanity.com/paper/1902.10162/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Survey on Active Feature Acquisition Strategies](https://mlsanity.com/paper/2502.11067/) (2025)
- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [Regression and Forecasting of U.S. Stock Returns Based on LSTM](https://mlsanity.com/paper/2502.05210/) (2025)
- [Fast Classification with Sequential Feature Selection in Test Phase](https://mlsanity.com/paper/2306.14347/) (2023)

## Abstract (arXiv)

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.