Adaptive Feature Evolution Learning with Multi-Label Evidence Transfer: Algorithm and Theory

Multi-label classification (MLC) conventionally assumes a stationary feature space, whereas real-world data streams often undergo simultaneous feature disappearance and emergence, disrupting label-discriminative evidence and rendering historical label dependencies increasingly mismatch. To address this chanllenges, we propose AFET, a unified framework that recasts dynamic-feature MLC as preserving and transferring label-relevant evidence across evolving feature spaces. Specifically, AFET exploits survived features as a cross-stage semantic anchor, recovering information associated with vanished features while preserving historical discriminative semantics through label-conditioned latent alignment. It further integrates recovered, survived and augmented evidence through instance-adaptive transformations, while adapting evolving label dependencies via structure-aware distillation and dynamically calibrating label-wise decision thresholds online. Theoretical analysis characterizes prediction stability across feature recovery, discriminative alignment, structural transfer and threshold adaptation. Extensive experiments on seven benchmarks under both one-shot and multi-shot settings demonstrate consistent improvements over competitive baselines.