Feature Weighting Improves Pool-Based Sequential Active Learning for Regression

TL;DR

This paper introduces feature weighting into pool-based sequential active learning for regression, significantly improving sample selection accuracy by incorporating feature importance into distance calculations.

Contribution

It proposes new feature weighted ALR methods that leverage ridge regression coefficients to enhance sample selection in regression tasks.

Findings

Feature weighting consistently improves ALR performance across multiple approaches.

The methods are effective in both single-task and multi-task regression problems.

The approach is simple, intuitive, and easily extendable to other ALR settings.

Abstract

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.