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Robust Stream Classification using Time Neutralising Decision Trees

Summary

The paper introduces Time Neutralising Trees (TNT), a decision tree architecture for robust stream classification under concept drift. TNT filters out outdated training samples during training and re-includes them if they become relevant again, achieving best-in-class performance on 24 real and semi-real data streams.

SourcearXiv Machine LearningAuthor: Brandon Gower-Winter, Georg Krempl
Robust Stream Classification using Time Neutralising Decision Trees
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[Submitted on 4 Nov 2026]

Title:Robust Stream Classification using Time Neutralising Decision Trees

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Abstract:Data Streams are characterised as a potentially infinite source of data subject to non-stationarity. This non-stationarity, also called Concept Drift, often results in deployed predictive models having to be perpetually retrained as the underlying data generating process of the stream changes over time. Drift Detectors are a class of methods used to identify these changes, triggering these retraining procedures. Historically, Stream Learning has operated under the assumption that instances are only seen once, and cannot be stored for later use. Contemporary work relaxes this assumption, yet drift detection and simple resetting procedures are still ubiquitous.

Building on related work which has shown that traditional Batch Learning algorithms often outperform Stream Learning algorithms when instances can be stored, we introduce Time Neutralising Trees (TNT), a Decision Tree architecture that enables robust stream classification in settings with Concept Drift. During training, TNT will filter out ("neutralise") old training samples that are no longer relevant given the current context of the stream. If those instances become relevant again, TNT will then re-include them. We evaluate TNT, and related algorithms on 24 real and semi-real classification data streams from the USP DS repository. Results show strong evidence that TNT achieves best-in-class performance on a wide-range of data streams with varying concept drift types.

Comments: 33 pages, 9 Tables, 10 Figures. Accepted at Neurips 2026

Subjects:

Machine Learning (cs.LG)

Cite as: arXiv:2611.06991 [cs.LG]

(or arXiv:2611.06991v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2611.06991

arXiv-issued DOI via DataCite

Journal reference: Advances in Neural Information Processing Systems - NeurIPS 2026

Submission history

From: Brandon Gower-Winter [view email] [v1] Wed, 4 Nov 2026 17:51:13 UTC (330 KB)

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Key points and analysis

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Key points

  • TNT addresses concept drift by neutralising old samples rather than relying on drift detectors to trigger retraining.
  • Filtered samples can be re-included if they become relevant again in the current stream context.
  • Evaluated on 24 data streams from the USP DS repository, TNT shows best-in-class performance.
  • The method builds on related work showing batch learning can outperform stream learning when instances are storable.

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

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