"ED2: A Case for Active
Learning in Error Detection"
by Felix Neutatz, Mohammad Mahdavi, and Ziawasch
Abedjan was accepted for CIKM 2019.
Here is the abstract:
formulate error detection as a semi-supervised classification problem.
Recent research suggests that active learning is insufficiently
effective for error detection and proposes the usage of neural
networks and data augmentation to reduce the number of these
user-provided labels. However, we can show that using the appropriate
active learning strategy, it is possible to outperform the more
complex models that rely on data augmentation. To this end, we propose
a multi-classifier approach with two-stage sampling for active
learning. This intuitive and neat sampling method chooses the most
promising cells across rows and columns for labeling. On three
datasets, ED2 achieves state-of-the-art detection accuracy while for
large datasets, the required number of user labels is lower by one
order of magnitude compared to the state of the