Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans "in-the-loop" have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans "over-the-loop", giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.
@article{arxiv.1712.00715,
title = {Formalizing Interruptible Algorithms for Human over-the-loop Analytics},
author = {Austin Graham and Yan Liang and Le Gruenwald and Christan Grant},
journal= {arXiv preprint arXiv:1712.00715},
year = {2017}
}
Comments
6 pages, 3 figures, Human-Machine Collaboration in Big Data