Despite the great promise of machine-learning algorithms to classify and
predict astrophysical parameters for the vast numbers of astrophysical sources
and transients observed in large-scale surveys, the peculiarities of the
training data often manifest as strongly biased predictions on the data of
interest. Typically, training sets are derived from historical surveys of
brighter, more nearby objects than those from more extensive, deeper surveys
(testing data). This sample selection bias can cause catastrophic errors in
predictions on the testing data because a) standard assumptions for
machine-learned model selection procedures break down and b) dense regions of
testing space might be completely devoid of training data. We explore possible
remedies to sample selection bias, including importance weighting (IW),
co-training (CT), and active learning (AL). We argue that AL---where the data
whose inclusion in the training set would most improve predictions on the
testing set are queried for manual follow-up---is an effective approach and is
appropriate for many astronomical applications. For a variable star
classification problem on a well-studied set of stars from Hipparcos and OGLE,
AL is the optimal method in terms of error rate on the testing data, beating
the off-the-shelf classifier by 3.4% and the other proposed methods by at least
3.0%. To aid with manual labeling of variable stars, we developed a web
interface which allows for easy light curve visualization and querying of
external databases. Finally, we apply active learning to classify variable
stars in the ASAS survey, finding dramatic improvement in our agreement with
the ACVS catalog, from 65.5% to 79.5%, and a significant increase in the
classifier's average confidence for the testing set, from 14.6% to 42.9%, after
a few AL iterations.

研究了机器学习算法在天文学中的应用，发现样本选择偏差是一大问题，探究了几种方法，其中主动学习是一种有效的方法，在变星分类问题上优于现有分类器和其他方法。