Active Learning (AL) methods seek to improve classifier performance when labels are expensive or scarce. We consider two central questions: Where does AL work? How much does it help? To address these questions, a comprehensive experimental simulation study of Active Learning is presented. We consider a variety of tasks, classifiers and other AL factors, to present a broad exploration of AL performance in various settings. A precise way to quantify performance is needed in order to know when AL works. Thus we also present a detailed methodology for tackling the complexities of assessing AL performance in the context of this experimental study.
@article{arxiv.1408.1319,
title = {When does Active Learning Work?},
author = {Lewis Evans and Niall M. Adams and Christoforos Anagnostopoulos},
journal= {arXiv preprint arXiv:1408.1319},
year = {2014}
}