# Sequential Cost-Sensitive Feature Acquisition

arXiv: https://arxiv.org/abs/1607.03691 · HTML: https://mlsanity.com/paper/1607.03691/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2016.

## Built on by (most cited first, 7 of 7)

- [Deep Reinforcement Learning for Cost-Effective Medical Diagnosis](https://mlsanity.com/paper/2302.10261/) (2023)
- [Learning Computational Efficient Bots with Costly Features](https://mlsanity.com/paper/2308.09629/) (2023)
- [Active Selection of Classification Features](https://mlsanity.com/paper/2102.13636/) (2021)
- [ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification](https://mlsanity.com/paper/2606.19371/) (2026)
- [Feature Acquisition using Monte Carlo Tree Search](https://mlsanity.com/paper/2212.11360/) (2022)
- [SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning](https://mlsanity.com/paper/2605.31014/) (2026)
- [Learning Source Acquisition Policies by Offline Planning](https://mlsanity.com/paper/2609.14299/) (2026)

## Abstract (arXiv)

We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled through a stochastic policy which allows features to be acquired in an adaptive way. The general architecture of our approach relies on representation learning to enable performing prediction on any partially observed sample, whatever the set of its observed features are. The resulting model is an original mix of representation learning and of reinforcement learning ideas. It is learned with policy gradient techniques to minimize a budgeted inference cost. We demonstrate the effectiveness of our proposed method with several experiments on a variety of datasets for the sparse prediction problem where all features have the same cost, but also for some cost-sensitive settings.