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

Summary

A new arXiv paper introduces PAI-KNN, a k-nearest neighbor classifier that adapts to concept drift and class imbalance by adjusting class priors at inference time rather than modifying memory, achieving competitive performance on synthetic streams and particularly strong results under dynamic imbalance.

SourcearXiv Machine LearningAuthor: Tobias Callies (University of the Bundeswehr Munich), Eirini Ntoutsi (University of the Bundeswehr Munich), Arthur Zimek (University of Southern Denmark)
Inference-Time Prior Adaptation for Imbalanced Data Streams under Concept Drift
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[Submitted on 23 Oct 2026]

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

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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.

Comments: 12 pages, 7 figures, accepted to Streaming Continual Learning Workshop at ECMLPKDD 2026

Subjects:

Machine Learning (cs.LG)

Cite as: arXiv:2610.33126 [cs.LG]

(or arXiv:2610.33126v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.33126

arXiv-issued DOI via DataCite

Submission history

From: Tobias Callies [view email] [v1] Fri, 23 Oct 2026 12:04:11 UTC (784 KB)

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Key points

  • PAI-KNN maintains a simple sliding-window buffer and dynamically adjusts its decision rule using class prior estimates from recent observations.
  • It avoids active memory management such as instance removal, memory restructuring, resampling, or synthetic data generation.
  • Evaluated on many synthetic streams with varying imbalance and drift, it performs competitively with state-of-the-art methods and especially well under dynamic class imbalance.
  • The findings suggest memory-based stream classifiers can adapt through inference-time adjustment without relying exclusively on memory modification.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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