  Supervised machine learning models are increasingly being used for solving
the problem of stellar classification of spectroscopic data. However, training
such models requires a large number of labelled instances, the collection of
which is usually costly in both time and expertise. Active learning algorithms
minimize training dataset sizes by keeping only the most informative instances.
This paper explores the application of active learning to sampling stellar
spectra using data from a highly class-imbalanced dataset. We utilize the
MaStar library from the SDSS DR17 along with its associated stellar parameter
catalogue. A preprocessing pipeline that includes feature selection, scaling,
and dimensionality reduction is applied to the data. Using different active
learning algorithms, we iteratively query instances, where the model or
committee of models exhibits the highest uncertainty or disagreement,
respectively. We assess the effectiveness of the sampling techniques by
comparing several performance metrics of supervised-learning models trained on
the queried samples with randomly-sampled counterparts. Evaluation metrics
include specificity, sensitivity, and the area under the curve; in addition to
the Matthew's correlation coefficient, which accounts for class imbalance. We
apply this procedure to effective temperature, surface gravity, and iron
metallicity, separately. Our results demonstrate the effectiveness of active
learning algorithms in selecting samples that produce performance metrics
superior to random sampling and even stratified samples, with fewer training
instances. Active learning is recommended for prioritizing instance labelling
of astronomical-survey data by experts or crowdsourcing to mitigate the high
time cost. Its effectiveness can be further exploited in selection of targets
for follow-up observations in automated astronomical surveys.
