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Found 6 papers, 1 papers with code
Detecting Concept Drift With Neural Network Model Uncertainty
Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model.
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
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.
Human vs. supervised machine learning: Who learns patterns faster?
We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find.
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
For efficient concept drift handling, we introduce the switching scheme which combines the two principles of retraining and updating of a machine learning model.
Handling Concept Drift for Predictions in Business Process Mining
However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model.
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Machine learning models are omnipresent for predictions on big data.
