Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
This work addresses the challenge of incremental concept drift for real-world applications like taxi demand prediction, but it appears incremental as it builds on existing adaptation strategies.
The paper tackles the problem of concept drift in deployed machine learning models by introducing a switching scheme that combines retraining and updating, and demonstrates its effectiveness by outperforming baselines on New York City taxi data with promising prediction results.
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 production. One challenge of deployed models is the effect of changing data over time, which is often described with the term concept drift. Due to their nature, concept drifts can severely affect the prediction performance of a machine learning system. In this work, we analyze the effects of concept drift in the context of a real-world data set. For efficient concept drift handling, we introduce the switching scheme which combines the two principles of retraining and updating of a machine learning model. Furthermore, we systematically analyze existing regular adaptation as well as triggered adaptation strategies. The switching scheme is instantiated on New York City taxi data, which is heavily influenced by changing demand patterns over time. We can show that the switching scheme outperforms all other baselines and delivers promising prediction results.