Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting
This addresses document classification in domains like legal and biomedical text with many infrequent classes and unpredictable drift, offering an incremental algorithmic improvement over existing methods.
The paper tackles multi-label document classification with hundreds of classes under temporal concept drift from unknown future events, finding that reframing group-robust optimization algorithms (Invariant Risk Minimization and Spectral Decoupling) as adaptation algorithms outperforms sampling-based approaches and significantly improves performance on minority classes, especially with larger label sets.
In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e.g., policy changes, conflicts, or pandemics. Class imbalance and drift can sometimes be mitigated by resampling the training data to simulate (or compensate for) a known target distribution, but what if the target distribution is determined by unknown future events? Instead of simply resampling uniformly to hedge our bets, we focus on the underlying optimization algorithms used to train such document classifiers and evaluate several group-robust optimization algorithms, initially proposed to mitigate group-level disparities. Reframing group-robust algorithms as adaptation algorithms under concept drift, we find that Invariant Risk Minimization and Spectral Decoupling outperform sampling-based approaches to class imbalance and concept drift, and lead to much better performance on minority classes. The effect is more pronounced the larger the label set.