Detecting virtual concept drift of regressors without ground truth values

DOI10.1007/S10618-021-00739-7zbMath1473.68158arXiv1910.04069OpenAlexW3129132077MaRDI QIDQ2036722

Henri Tiittanen, Andreas Henelius, Emilia Oikarinen, Kai Puolamäki

Publication date: 30 June 2021

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.04069

zbMATH Keywords

concept driftgeneralization errorunknown ground truth

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

SLISEMAP: supervised dimensionality reduction through local explanations

Uses Software

• ElemStatLearn
• GitHub
• fpp2

Cites Work

• Tracking concept drift using a constrained penalized regression combiner
• Generalization bounds for non-stationary mixing processes
• New Analysis and Algorithm for Learning with Drifting Distributions
• A scalable gaussian process analysis algorithm for biomass monitoring
• A survey on concept drift adaptation

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