Inference-Time Prior Adaptation for Imbalanced Data Streams under Concept Drift

Computer Science > Machine Learning

[Submitted on 23 Oct 2026]

Title:Inference-Time Prior Adaptation for Imbalanced Data Streams under Concept Drift

View PDF HTML (experimental)Abstract:Data stream classification under concept drift and class imbalance requires models to adapt continuously while maintaining strong minority-class performance. Many existing approaches rely on active memory management, in- cluding instance removal, memory restructuring, resampling, and synthetic data generation. We present PAI-KNN, a k-nearest neighbor classifier that adapts via inference-time prior adjustment rather than memory modification. PAI-KNN main- tains a simple sliding-window buffer and dynamically adjusts its decision rule using class prior estimates derived from recent observations. Evaluated on a mul- titude of synthetic data streams covering various imbalance and drift scenarios, PAI-KNN achieves competitive performance with state-of-the-art methods, per- forming particularly well under scenarios involving dynamic class imbalance and concept drift. These findings highlight the potential of inference-time adaptation in challenging non-stationary environments and suggest that adaptation in memory- based stream classifiers need not rely exclusively on memory modification.

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