# Feature Weighting Improves Pool-Based Sequential Active Learning for Regression

arXiv: https://arxiv.org/abs/2604.02019 · HTML: https://mlsanity.com/paper/2604.02019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [Pool-Based Sequential Active Learning for Regression](https://mlsanity.com/paper/1805.04735/) (2018)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Active Learning for Regression Using Greedy Sampling](https://mlsanity.com/paper/1808.04245/) (2018)
- [Stream-based active learning with linear models](https://mlsanity.com/paper/2207.09874/) (2022)
- [Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)](https://mlsanity.com/paper/2003.07658/) (2020)
- [A Graph-Based Approach for Active Learning in Regression](https://mlsanity.com/paper/2001.11143/) (2020)
- [Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression](https://mlsanity.com/paper/1808.04244/) (2018)
- [Unsupervised Pool-Based Active Learning for Linear Regression](https://mlsanity.com/paper/2001.05028/) (2020)
- [Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration](https://mlsanity.com/paper/1805.05781/) (2018)
- [Streaming Active Learning for Regression Problems Using Regression via Classification](https://mlsanity.com/paper/2309.01013/) (2023)
- [Semi-Supervised Transfer Boosting (SS-TrBoosting)](https://mlsanity.com/paper/2412.03212/) (2024)

## Abstract (arXiv)

Pool-based sequential active learning for regression (ALR) optimally selects a small number of samples sequentially from a large pool of unlabeled samples to label, so that a more accurate regression model can be constructed under a given labeling budget. Representativeness and diversity, which involve computing the distances among different samples, are important considerations in ALR. However, previous ALR approaches do not incorporate the importance of different features in inter-sample distance computation, resulting in inaccurate distances and hence sub-optimal sample selection. This paper proposes four feature weighted single-task ALR approaches and three feature weighted multi-task ALR approaches, where the ridge regression coefficients trained from a small amount of previously labeled samples are used to weight the corresponding features in inter-sample distance computation. Extensive experiments showed that this intuitive and easy-to-implement enhancement almost always improves the performance of five existing ALR approaches, in both single-task and multi-task regression problems. The feature weighting strategy may also be easily extended to stream-based ALR, and classification algorithms.