A Comparative Study on Concept Drift Detectors for Regression - Lima, Marília; Silva Filho, Telmo; Fagundes, Roberta Andrade de A. (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2021: Anais da X Brazilian Conference on Intelligent Systems, 2021)
Context: in the field of machine learning models are trained to learn from data, however often the context at which a model is deployed changes, degrading the performances of trained models and giving rise to a problem called Concept Drift (CD), which is a change in data distribution. Motivation: CD has attracted attention in machine learning literature, with works proposing modification to well-known algorithms’ structures, ensembles, online learning and drift detection, but most of the CD literature regards classification, while regression drift is still poorly explored. Objective: The goal of this work is to perform a comparative study of CD detectors in the context of regression. Results: we found that (i) PH, KSWIN and EDDM showed higher detection averages; (ii) the base learner has a strong impact in CD detection and (iii) the rate at which CD happens also affects the detection process. Conclusion: our experiments were executed in a framework that can easily be extended to include new CD detectors and base learners, allowing future studies to use it.