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This paper addresses sub-optimal sample selection in pool-based sequential active learning for regression (ALR) due to the failure of existing methods to account for feature importance when calculating inter-sample distances. They propose weighting features based on ridge regression coefficients learned from previously labeled samples to improve representativeness and diversity considerations in ALR. Empirical results demonstrate that this feature weighting enhancement consistently improves the performance of existing ALR approaches in both single-task and multi-task regression settings.
Simple feature weighting, derived from ridge regression coefficients, unlocks significant performance gains in pool-based 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 sub-optimal sample selection. This paper proposes three feature weighted single-task ALR approaches and two 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. Experiments showed that this easy-to-implement enhancement almost always improves the performance of four 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.