A Self-Tuning ensemble approach for drift detection

  • Guilherme Y. Sakurai UEL
  • Bruno B. Zarpelão UEL
  • Sylvio Barbon Junior University of Trieste

Resumo


Processing data streams is challenging due to the need for mining algorithms to adapt to real-time drifts. Ensemble strategies for concept drift detection show promise, yet gaps in flexibility and detection remain. We propose the Self-tuning Drift Ensemble (StDE) method, which dynamically adapts ensemble structure to stream changes while maintaining a lightweight solution. StDE adjusts the number of base learners through a self-regulating voting system, achieving high detection accuracy. Experiments across various drift scenarios demonstrate the superior performance of our method compared to established baselines.

Palavras-chave: Data Stream, Drift, Ensemble

Referências

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Publicado
17/11/2024
SAKURAI, Guilherme Y.; ZARPELÃO, Bruno B.; BARBON JUNIOR, Sylvio. A Self-Tuning ensemble approach for drift detection. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 811-822. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2024.245158.