  In many real-world machine learning applications, unlabeled samples are easy
to obtain, but it is expensive and/or time-consuming to label them. Active
learning is a common approach for reducing this data labeling effort. It
optimally selects the best few samples to label, so that a better machine
learning model can be trained from the same number of labeled samples. This
paper considers active learning for regression (ALR) problems. Three essential
criteria -- informativeness, representativeness, and diversity -- have been
proposed for ALR. However, very few approaches in the literature have
considered all three of them simultaneously. We propose three new ALR
approaches, with different strategies for integrating the three criteria.
Extensive experiments on 12 datasets in various domains demonstrated their
effectiveness.
