Bhattacharyya distance based concept drift detection method for evolving data stream

The majority of online learners assume that the data distribution to be learned is established in advance. There are many real-world problems where the distribution of the data changes as a function of time. Variations in data streams data distributions can sufficiently reduce the skill of the learning algorithm on new data, if the learning algorithm is not equipped to track such changes. Therefore, the algorithm should initiate required actions to make sure that the new information is learned properly. In this article, we propose Bhattacharyya Distance-based Concept Drift Detection Method (BDDM) which uses Bhattacharyya distance to identify gradual or abrupt variations in the distribution. Experiments executed in the MOA framework using three artificial data generators and ten real-world datasets suggest that BDDM improves the detections and accuracies in many scenarios.

Automated summary

The article discusses the changing distribution of data over time, proposes a method using Bhattacharyya distance for concept drift detection, and demonstrates through experiments that it improves detection and accuracy in various scenarios.