# Stream-based active learning with linear models

arXiv: https://arxiv.org/abs/2207.09874 · HTML: https://mlsanity.com/paper/2207.09874/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 53 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Online Active Linear Regression via Thresholding](https://mlsanity.com/paper/1602.02845/) (2016)

## Built on by (most cited first, 9 of 9)

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders](https://mlsanity.com/paper/2212.13067/) (2022)
- [Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss](https://mlsanity.com/paper/2309.05517/) (2023)
- [Robust online active learning](https://mlsanity.com/paper/2302.00422/) (2023)
- [Streaming Active Learning for Regression Problems Using Regression via Classification](https://mlsanity.com/paper/2309.01013/) (2023)
- [Camera clustering for scalable stream-based active distillation](https://mlsanity.com/paper/2404.10411/) (2024)
- [Data-driven jet fuel demand forecasting: A case study of Copenhagen Airport](https://mlsanity.com/paper/2511.05569/) (2025)
- [Feature Weighting Improves Pool-Based Sequential Active Learning for Regression](https://mlsanity.com/paper/2604.02019/) (2026)
- [QueryMarket: Cost-Aware Online Active Learning in Data Markets](https://mlsanity.com/paper/2606.17805/) (2026)

## Abstract (arXiv)

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.