Feature Weighting Improves Pool-Based Sequential Active Learning for Regression
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...
| Authors | Dongrui Wu |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 4 May 2026 |
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