A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
This is an incremental review that synthesizes existing research to help researchers and practitioners address data change issues in AI deployment.
The paper tackles the challenge of AI model performance degradation due to dynamic data changes by unifying domain shift and concept drift into a single data change problem, providing a systematic review and categorization of state-of-the-art methods.
Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges.