Enhancing Concept Drift Detection in Drifting and Imbalanced Data Streams through Meta-Learning

One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that makes previously learned knowledge and model outdated. Therefore, concept drift must be efficiently detected in order to adapt the classification model. While there exists a plethora of drift detectors, with different mechanisms, selecting the most suitable for a new stream is a difficult task, since apriori knowledge may not be available and changes over time can affect the performance of the detector. This paper proposes a framework that exploits statistical and temporal meta-features from sliding windows to dynamically recommend a suitable drift detector in real-time for unseen chunks of streams according to its properties using Meta-Learning. We performed experiments on 10 real-world data streams and 18 synthetic generated data streams that were subject to concept drift and class imbalance in order to evaluate the performance of the proposed framework. Experiments exposed that the proposed approach was able to enhance the concept drift detection in a variety of scenarios demonstrating robustness to class imbalance and the advantages of dynamically selecting the drift detector.