UCAT - U-test certainty adaptive tree for regression on non-stationary data streams

Publication date
2026-05-07
Document type
Konferenzbeitrag
Author
Stratmann, Benedikt
Hermes, Jan
Constanze, Hasterok
Organisational unit
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)
DOI
URI
Conference
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2026
First page
1
Last page
18
Is part of
Peer-reviewed
✅
Part of the university bibliography
Nein
File(s)
Language
English
Keyword
Non-stationary data streams
Online regression
Adaptive model trees
Concept drift detection
Wilcoxon–Mann–Whitney U-test
Abstract
Cyber-physical systems (CPS) often rely on learned surrogate models whose performance degrades under non-stationary conditions. While many adaptive classifiers exist, adaptive methods for tabular regression are scarce. We propose UCAT, an adaptive model tree for online regression on non-stationary data streams in CPS. UCAT replaces the Hoeffding bound used by existing adaptive trees with a one-sided Wilcoxon–Mann–Whitney U-test on absolute prediction errors to select splits, and combines residual model trees with local linear models, Page-Hinkley drift detection, rival branches for subtree replacement, and exposure-based pruning. An optional twig-level threshold adaptation further refines split thresholds. On the SiD2Re benchmark (15 datasets, 7.680 stream variants), we compare UCAT and a twig-adapting variant (UCAT_twig) with FIMT-DD using prequential evaluation. UCAT achieves lower cumulative absolute error on 68% of streams, with an average reduction of 13.33%, and is preferred by normalized error metrics on most datasets, whereas FIMT-DD performs best mainly in stationary or high-noise regimes. These gains come at increased computational cost: UCAT is about 43% slower than FIMT-DD. Overall, UCAT provides an interpretable, streaming-capable regression method with statistically grounded split selection and targeted adaptation for CPS, suitable when improved accuracy and drift handling justify higher runtime.
Description
This contribution is part of the conference proceedings, which are licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)
Version
Published version
Access right on openHSU
Open access