Tracking Recurring Concepts from Evolving Data Streams using Ensemble Method

Ensemble models are the most widely used methods for classifying evolving data stream. However, most of the existing data stream ensemble classification algorithms do not consider the issue of recurring concepts, which commonly exist in real-world applications. Motivated by this challenge, an Ensemble with internal Change Detection (ECD) was proposed to enhance performance by exploring the recurring concepts. It is done by maintaining a pool of classifiers, which dynamically adds and removes classifiers in response to the change detector. The algorithm adopts a two window change detection model, which adopts the Jensen-Shannon divergence to measure the distance of the distributions between old and recent data. When a change is detected, the repository of stored historical concepts is checked for reuse. Experimental results on both synthetic and real-world data streams demonstrate that the proposed algorithm not only outperforms the state-of-art methods on standard evaluation metrics, but also adapts well in different types of concept drift scenarios especially when concept s reappear.