Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice

Xide Xia, Pavlos Protopapas, Finale Doshi-Velez

Abstract: Astronomers and telescope operators must make decisions about what to observe given limited telescope time. To optimize this decision-making process, we present a batch, cost-sensitive, active learning approach that exploits structure in the unlabeled dataset, accounts for label uncertainty, and minimizes annotation costs. We first cluster the unlabeled instances in feature space. We next introduce an uncertainty-reducing selection criterion that encourages the batch of selected instances to span multiple clusters, in addition to taking into account annotation cost. Finally, we extend this criterion to incorporate the fact that nearby astronomical objects may be observed at the same time. On two large astronomical data sets, our approach balances the trade-offs among FOV, aperture, and time cost and, therefore, helps astronomers design effective experiments.