Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets

TL;DR

This paper introduces a switching scheme that combines retraining and updating to effectively handle concept drift in real-world data, demonstrated on NYC taxi data, outperforming existing methods.

Contribution

The paper proposes a novel switching scheme for concept drift management that integrates retraining and updating strategies, validated on real-world data.

Findings

Switching scheme outperforms baseline methods.

Effective handling of demand-driven concept drift.

Promising prediction accuracy improvements.

Abstract

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.