Understanding Concept Drift
This work addresses concept drift, a critical issue affecting accuracy in real-world ML applications, but it appears incremental as it focuses on descriptive tools rather than new solutions.
The paper tackles the problem of concept drift in machine learning by proposing tools for quantitative description and analysis of drift in marginal distributions, demonstrating their effectiveness on three real-world tasks.
Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is important to develop the capacity to describe and analyze it. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift in marginal distributions. We present quantitative drift analysis techniques along with methods for communicating their results. We demonstrate their effectiveness by application to three real-world learning tasks.