AutoDC: Automated data-centric processing
This addresses the inefficiency in dataset enhancement for machine learning practitioners, though it appears incremental as it automates existing data-centric tasks.
The paper tackles the problem of manual and expensive data improvement processes in machine learning by developing AutoDC, an automated data-centric tool. In preliminary tests on three image classification datasets, it reduces manual time by about 80% and improves model accuracy by 10-15% with fixed ML code.
AutoML (automated machine learning) has been extensively developed in the past few years for the model-centric approach. As for the data-centric approach, the processes to improve the dataset, such as fixing incorrect labels, adding examples that represent edge cases, and applying data augmentation, are still very artisanal and expensive. Here we develop an automated data-centric tool (AutoDC), similar to the purpose of AutoML, aims to speed up the dataset improvement processes. In our preliminary tests on 3 open source image classification datasets, AutoDC is estimated to reduce roughly 80% of the manual time for data improvement tasks, at the same time, improve the model accuracy by 10-15% with the fixed ML code.