ED2: Two-stage Active Learning for Error Detection -- Technical Report
This work addresses error detection for data users by reducing the need for technical knowledge, though it appears incremental as it builds on active learning techniques.
The paper tackles the problem of error detection in data by formulating it as a semi-supervised classification task, requiring only domain expertise instead of user-defined parameters. It introduces ED2, a method that uses a two-dimensional multi-classifier sampling strategy and multi-column features, achieving high detection accuracy with less than 1% labels on average to outperform existing approaches.
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.