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New Time Neutralising Trees improve stream classification with concept drift

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Time Neutralising Trees (TNT), a novel decision tree architecture designed to improve stream classification performance in the presence of concept drift. TNT addresses non-stationarity by filtering out outdated training samples that are no longer relevant to the current data stream context, while also re-including them if they become relevant again. Evaluations on 24 real and semi-real data streams from the USP DS repository indicate that TNT outperforms existing algorithms across various types of concept drift. AI

IMPACT Introduces a novel approach to handling concept drift in stream classification, potentially improving the robustness of deployed models.

RANK_REASON Academic paper introducing a new algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Time Neutralising Trees improve stream classification with concept drift

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Academic paper introducing a new algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Brandon Gower-Winter, Georg Krempl ·

    Robust Stream Classification using Time Neutralising Decision Trees

    arXiv:2611.06991v1 Announce Type: new 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…

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