# Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

arXiv: https://arxiv.org/abs/1901.00243 · HTML: https://mlsanity.com/paper/1901.00243/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 20 of 20)

- [Learning to Maximize Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2301.00557/) (2023)
- [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)
- [Stochastic Encodings for Active Feature Acquisition](https://mlsanity.com/paper/2508.01957/) (2025)
- [AFABench: A Generic Framework for Benchmarking Active Feature Acquisition](https://mlsanity.com/paper/2508.14734/) (2025)
- [How Important is Importance Sampling for Deep Budgeted Training?](https://mlsanity.com/paper/2110.14283/) (2021)
- [Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning](https://mlsanity.com/paper/2210.07198/) (2022)
- [Learning-To-Measure: In-Context Active Feature Acquisition](https://mlsanity.com/paper/2510.12624/) (2025)
- [Constrained Multiview Representation for Self-supervised Contrastive Learning](https://mlsanity.com/paper/2402.03456/) (2024)
- [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)
- [Dynamic Feature Selection from Variable Feature Sets Using Features of Features](https://mlsanity.com/paper/2503.09181/) (2025)
- [DiaLLMs: EHR Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction](https://mlsanity.com/paper/2506.20059/) (2025)
- [Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients](https://mlsanity.com/paper/2605.05511/) (2026)
- [Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2206.03659/) (2022)
- [BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition](https://mlsanity.com/paper/2608.02305/) (2026)
- [Active Feature Acquisition With Incomplete Training Data](https://mlsanity.com/paper/2609.32325/) (2026)
- [Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition](https://mlsanity.com/paper/2610.03454/) (2026)
- [AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints](https://mlsanity.com/paper/2610.07615/) (2026)

## Abstract (arXiv)

In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature. Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function. Furthermore, we suggest sharing representations between the class predictor and value function estimator networks. The suggested approach is completely online and is readily applicable to stream learning setups. The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification. According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.