Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
This addresses the challenge of maintaining prediction accuracy for deployed machine learning models in dynamic environments, such as urban transportation, but is incremental in its method adaptation.
The paper tackles the problem of concept drift in regression tasks by proposing a novel approach that switches between simple and complex models based on drift detection, and demonstrates significant outperformance over baselines on a real-world taxi demand dataset in New York City.
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 handled correctly, a concept drift can lead to significant mispredictions. We explore a novel approach for concept drift handling, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks. We assume that the approach plays out the individual strengths of each model, switching to the simpler model if a drift occurs and switching back to the complex model for typical situations. We instantiate the approach on a real-world data set of taxi demand in New York City, which is prone to multiple drifts, e.g. the weather phenomena of blizzards, resulting in a sudden decrease of taxi demand. We are able to show that our suggested approach outperforms all regarded baselines significantly.