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

@inproceedings{Xia2016CostSensitiveBM,
  title={Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice},
  author={Xide Xia and Pavlos Protopapas and Finale Doshi-Velez},
  booktitle={SDM},
  year={2016}
}
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… 

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