One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift
The world surrounding us is subject to constant change. These changes, frequently
described as concept drift, influence many industrial and technical processes. As they can …
described as concept drift, influence many industrial and technical processes. As they can …
One or Two Things We know about Concept Drift--A Survey on Monitoring Evolving Environments
The world surrounding us is subject to constant change. These changes, frequently
described as concept drift, influence many industrial and technical processes. As they can …
described as concept drift, influence many industrial and technical processes. As they can …
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift
In an increasing number of industrial and technical processes, machine learning-based
systems are being entrusted with supervision tasks. While they have been successfully …
systems are being entrusted with supervision tasks. While they have been successfully …
Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations
Facing climate change, the already limited availability of drinking water will decrease in the
future, rendering drinking water an increasingly scarce resource. Considerable amounts of it …
future, rendering drinking water an increasingly scarce resource. Considerable amounts of it …
isage: An incremental version of SAGE for online explanation on data streams
Existing methods for explainable artificial intelligence (XAI), including popular feature
importance measures such as SAGE, are mostly restricted to the batch learning scenario …
importance measures such as SAGE, are mostly restricted to the batch learning scenario …
Concept Drift Visualization of SVM with Shifting Window
H Gâlmeanu, R Andonie - 2024 28th International Conference …, 2024 - ieeexplore.ieee.org
In machine learning, concept drift is an evolution of information that invalidates the current
data model. It happens when the statistical properties of the input data change over time in …
data model. It happens when the statistical properties of the input data change over time in …
Detecting and rationalizing concept drift: A feature-level approach for understanding cause–effect relationships in dynamic environments
L Yang, J Cheng, Y Luo, T Zhou, X Zhang - Expert Systems with …, 2025 - Elsevier
Abstract Concept drift detection is essential for data-driven models to adapt to changing data
patterns, ensuring accuracy and reliability in dynamic environments. Explaining drift can be …
patterns, ensuring accuracy and reliability in dynamic environments. Explaining drift can be …
[HTML][HTML] Feature-based analyses of concept drift
Feature selection is one of the most relevant preprocessing and analysis techniques in
machine learning. It can dramatically increase the performance of learning algorithms and at …
machine learning. It can dramatically increase the performance of learning algorithms and at …
Localization of Small Leakages in Water Distribution Networks using Concept Drift Explanation Methods
Facing climate change the already limited availability of drinking water will decrease in the
future rendering drinking water an increasingly scarce resource. Considerable amounts of it …
future rendering drinking water an increasingly scarce resource. Considerable amounts of it …
Spurious Correlations in Concept Drift: Can Explanatory Interaction Help?
C Lalletti, S Teso - arXiv preprint arXiv:2407.16515, 2024 - arxiv.org
Long-running machine learning models face the issue of concept drift (CD), whereby the
data distribution changes over time, compromising prediction performance. Updating the …
data distribution changes over time, compromising prediction performance. Updating the …