# Classification with Costly Features as a Sequential Decision-Making Problem

arXiv: https://arxiv.org/abs/1909.02564 · HTML: https://mlsanity.com/paper/1909.02564/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 23 of 23)

- [Estimating Conditional Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2306.03301/) (2023)
- [A Survey on Active Feature Acquisition Strategies](https://mlsanity.com/paper/2502.11067/) (2025)
- [Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task](https://mlsanity.com/paper/2211.05039/) (2022)
- [DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations](https://mlsanity.com/paper/2205.03755/) (2022)
- [Combining Cost-Constrained Runtime Monitors for AI Safety](https://mlsanity.com/paper/2507.15886/) (2025)
- [Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings](https://mlsanity.com/paper/2312.01530/) (2023)
- [AFABench: A Generic Framework for Benchmarking Active Feature Acquisition](https://mlsanity.com/paper/2508.14734/) (2025)
- [Cohort-Based Active Modality Acquisition](https://mlsanity.com/paper/2505.16791/) (2025)
- [Evaluation of Active Feature Acquisition Methods for Static Feature Settings](https://mlsanity.com/paper/2312.03619/) (2023)
- [Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks](https://mlsanity.com/paper/2104.09650/) (2021)
- [Spatial State-Action Features for General Games](https://mlsanity.com/paper/2201.06401/) (2022)
- [Dynamic Instance-Wise Classification in Correlated Feature Spaces](https://mlsanity.com/paper/2106.04668/) (2021)
- [NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization](https://mlsanity.com/paper/2206.00906/) (2022)
- [NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data](https://mlsanity.com/paper/2507.12412/) (2025)
- [Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients](https://mlsanity.com/paper/2605.05511/) (2026)
- [Evolutionary Optimization of High-Coverage Budgeted Classifiers](https://mlsanity.com/paper/2110.13067/) (2021)
- [Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2206.03659/) (2022)
- [Online Learning with Costly Features in Non-stationary Environments](https://mlsanity.com/paper/2307.09388/) (2023)
- [Relaxed Efficient Acquisition of Context and Temporal Features](https://mlsanity.com/paper/2603.11370/) (2026)
- [Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization](https://mlsanity.com/paper/2606.12251/) (2026)
- [Learning Source Acquisition Policies by Offline Planning](https://mlsanity.com/paper/2609.14299/) (2026)
- [Active Feature Acquisition With Incomplete Training Data](https://mlsanity.com/paper/2609.32325/) (2026)
- [Should I stay or should I show? Learning to selectively disclose information](https://mlsanity.com/paper/2609.39818/) (2026)

## Abstract (arXiv)

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be solved with deep reinforcement learning. Also, we evaluate a real-world inspired setting with sparse training dataset with missing features. The presented method performs robustly well in all settings across several distinct datasets, outperforming other prior-art algorithms. The method is flexible, as showcased with all mentioned modifications and can be improved with any domain independent advancement in RL.