DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
Published in AAAI, 2022
Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian
Paper link: [arXiv] [CODE] [POSTER] [REPORT (Chinese)]
- Proposed a meta-learning method DDG-DA for concept drift adaptation. The meta-model learns to guide the retraining process of the base model by sample reweighting and aims to improve the performance of the base model on unseen test data.
- The meta-model improved performance by $11.3\%$ in the signal-based metrics, and $46.7\%$ in the portfolio-based metrics compared to the baseline.