Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
This work addresses concept drift in data streams for regression, which is a domain-specific problem, and is incremental as it builds on an existing method.
The paper tackles concept drift detection in regression tasks by improving the Scale-free Network Regressor (SFNR), a dynamic ensemble method based on social networks theory, resulting in enhanced accuracy and better performance compared to state-of-the-art algorithms in drift scenarios.
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classification task but usually maintain a fixed size ensemble of learners running the risk of needlessly spending processing time and memory. In this paper we present improvements to the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for regression that employs social networks theory. In order to detect concept drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show improvements in accuracy, especially in concept drift situations and better performance compared to other state-of-the-art algorithms in both real and synthetic data.