  Traditional error detection approaches require user-defined parameters and
rules. Thus, the user has to know both the error detection system and the data.
However, we can also formulate error detection as a semi-supervised
classification problem that only requires domain expertise. The challenges for
such an approach are twofold: (1) to represent the data in a way that enables a
classification model to identify various kinds of data errors, and (2) to pick
the most promising data values for learning. In this paper, we address these
challenges with ED2, our new example-driven error detection method. First, we
present a new two-dimensional multi-classifier sampling strategy for active
learning. Second, we propose novel multi-column features. The combined
application of these techniques provides fast convergence of the classification
task with high detection accuracy. On several real-world datasets, ED2
requires, on average, less than 1% labels to outperform existing error
detection approaches. This report extends the peer-reviewed paper "ED2: A Case
for Active Learning in Error Detection". All source code related to this
project is available on GitHub.
