The Blind Spot Paradox: When Adaptive Classifiers Defeat Drift Detectors

Abstract:Monitoring concept drift from an adaptive classifier's error stream creates an operational conflict with the model's own update loop. When internal adaptation outpaces evidence accumulation, accuracy recovers before cumulative detectors (CUSUM, Page-Hinkley) can reach threshold. Instrumenting an Adaptive Random Forest (ARF) shows that surviving trees absorb 98.6% of the post-drift error transient through incremental leaf updates alone. The first background tree swap accounts for just 0.71% of this erased error volume, but drops external detection rates by 31 percentage points. We derive the finite-horizon boundary where cumulative evidence fails to cross threshold and measure a critical magnitude floor ($\Delta e_c = 0.120$) below which false-alarm budgets preclude detection. This failure manifests as missed shifts on stationary streams and false-alarm flooding triggered by internal tree swaps on noisy baselines. We validate on synthetic shifts, ARMA-GARCH series (ProteuS), and tabular benchmarks (BAF, INSECTS); on the synthetic sweep at a standard threshold, the blind spot appears at $\Delta e \approx 0.25$, showing why classical benchmarks like SEA ($\Delta e \le 0.21$) failed to reach it.

Accepted at IEEE ICDM 2026 Workshops (OWAD 2026). 10 pages, 3 figures, 2 tables. Code: this https URL

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2610.05853 [cs.LG]

(or arXiv:2610.05853v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.05853 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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