ED2: Two-stage Active Learning for Error Detection -- Technical Report

TL;DR

ED2 introduces a semi-supervised, active learning approach with novel sampling and features for error detection, achieving high accuracy with minimal labeled data on real datasets.

Contribution

The paper presents ED2, a new two-stage active learning method with multi-column features that improves error detection efficiency and accuracy.

Findings

ED2 requires less than 1% labels to outperform existing methods

Fast convergence of classification with high detection accuracy

Effective on multiple real-world datasets

Abstract

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.