A Comprehensive Review of Machine Learning Advances on Data Change: A
  Cross-Field Perspective
Jeng-Lin Li Chih-Fan Hsu Ming-Ching Chang Wei-Chao Chen
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.