Machine learning algorithms have become increasingly prevalent in multiple
domains, such as autonomous driving, healthcare, and finance. In such domains,
data preparation remains a significant challenge in developing accurate models,
requiring significant expertise and time investment to search the huge search
space of well-suited data curation and transformation tools. To address this
challenge, we present AutoCure, a novel and configuration-free data curation
pipeline that improves the quality of tabular data. Unlike traditional data
curation methods, AutoCure synthetically enhances the density of the clean data
fraction through an adaptive ensemble-based error detection method and a data
augmentation module. In practice, AutoCure can be integrated with open source
tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of
machine learning. As a proof of concept, we provide a comparative evaluation of
AutoCure against 28 combinations of traditional data curation tools,
demonstrating superior performance and predictive accuracy without user
intervention. Our evaluation shows that AutoCure is an effective approach to
automating data preparation and improving the accuracy of machine learning
models.

AutoCure 是一种自动化的数据准备方法，通过集成基于集成模型的错误检测方法和数据增强模块，可以提高标签数据的质量，加快机器学习模型的准备过程，无需用户干预。相比传统的数据准备工具，AutoCure 在 28 种不同的数据准备方法中获得了优异的表现和预测准确性。