Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives

Guilherme Yukio Sakurai, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão and Sylvio Barbon Junior ()
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Guilherme Yukio Sakurai: Department of Computer Science, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil
Jessica Fernandes Lopes: Department of Electrical Engineering, State University of Londrina, Londrina 86057-970, PR, Brazil
Bruno Bogaz Zarpelão: Department of Computer Science, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil
Sylvio Barbon Junior: Department of Engineering and Architecture, University of Trieste (UNITS), 34127 Trieste, Italy

Future Internet, 2023, vol. 15, issue 5, 1-17

Abstract: The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance.

Keywords: concept drift; drift detector; adaptive learning; synthetic datasets; stream mining (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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