A Comparative Study on Concept Drift Detectors for Regression

• Marília Lima UPE https://orcid.org/0000-0002-6755-2649
• Telmo Silva Filho UFPB https://orcid.org/0000-0003-0826-6885
• Roberta Andrade de A. Fagundes UPE https://orcid.org/0000-0002-7172-4183

Resumo

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.

Palavras-chave: Concept drift detection, Regression, Comparative study