  Technology-Assisted Review (TAR) aims to reduce the human effort required for
screening processes such as abstract screening for systematic literature
reviews. Human reviewers label documents as relevant or irrelevant during this
process, while the system incrementally updates a prediction model based on the
reviewers' previous decisions. After each model update, the system proposes new
documents it deems relevant, to prioritize relevant documentsover irrelevant
ones. A stopping criterion is necessary to guide users in stopping the review
process to minimize the number of missed relevant documents and the number of
read irrelevant documents. In this paper, we propose and evaluate a new
ensemble-based Active Learning strategy and a stopping criterion based on
Chao's Population Size Estimator that estimates the prevalence of relevant
documents in the dataset. Our simulation study demonstrates that this criterion
performs well on several datasets and is compared to other methods presented in
the literature.
