# Feature Acquisition using Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/2212.11360 · HTML: https://mlsanity.com/paper/2212.11360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 21 Dec 2022.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Active Feature Acquisition with Generative Surrogate Models](https://mlsanity.com/paper/2010.02433/) (2020)
- [Active Feature Acquisition with Supervised Matrix Completion](https://mlsanity.com/paper/1802.05380/) (2018)
- [Convex Hull Monte-Carlo Tree Search](https://mlsanity.com/paper/2003.04445/) (2020)
- [Sequential Cost-Sensitive Feature Acquisition](https://mlsanity.com/paper/1607.03691/) (2016)

## Abstract (arXiv)

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.