18 citations · 56 across the 6 of their papers we have counts for
6 papers
Detecting Concept Drift With Neural Network Model Uncertainty
Lucas Baier, Tim Schlör, Jakob Schöffer +1
Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift dete…
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
Lucas Baier, Niklas Kühl, Jakob Schöffer +1
As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic. However, it r…
Human vs. supervised machine learning: Who learns patterns faster?
Niklas Kühl, Marc Goutier, Lucas Baier +2
The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provid…
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
Lucas Baier, Vincent Kellner, Niklas Kühl +1
Machine learning models nowadays play a crucial role for many applications in business and industry. However, models only start adding value as soon as they are deployed into produ…
Handling Concept Drift for Predictions in Business Process Mining
Lucas Baier, Josua Reimold, Niklas Kühl
Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which i…
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Lucas Baier, Marcel Hofmann, Niklas Kühl +2
Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not…