19 citations · 51 across the 26 of their papers we have counts for
5 papers · 1 filter
A Remark on Concept Drift for Dependent Data
Fabian Hinder, Valerie Vaquet, Barbara Hammer
Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the str…
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments
Fabian Hinder, Valerie Vaquet, Barbara Hammer
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 lead to…
Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations
Valerie Vaquet, Fabian Hinder, Jonas Vaquet +3
Facing climate change, the already limited availability of drinking water will decrease in the future rendering drinking water an increasingly scarce resource. Considerable amounts…
Model Based Explanations of Concept Drift
Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf +1
The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become i…
Combining self-labeling and demand based active learning for non-stationary data streams
Valerie Vaquet, Fabian Hinder, Johannes Brinkrolf +1
Learning from non-stationary data streams is a research direction that gains increasing interest as more data in form of streams becomes available, for example from social media, s…