Research on concept drift data stream classification based on adaptive online broad learning system

Shengkai Sun; Wei Guo

doi:10.1117/12.3003781

11 October 2023 Research on concept drift data stream classification based on adaptive online broad learning system

Shengkai Sun, Wei Guo

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280069 (2023) https://doi.org/10.1117/12.3003781
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Traditional online broad learning system are unable to adapt to dynamic changes in data streams for concept drift data stream classification. To address this issue, this paper proposes an Adaptive Online Broad Learning System (AOBLS) classification algorithm that introduces an adaptive forgetting factor and concept drift detection mechanism. Additionally, this paper proposes a concept drift index to measure the degree of concept drift. By combining the concept drift index and forgetting factor, the model can adaptively adjust the size of the forgetting factor to better handle concept drift. Experimental results demonstrate that the proposed algorithm performs better than similar algorithms in terms of classification accuracy, stability, and concept drift adaptability.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengkai Sun and Wei Guo "Research on concept drift data stream classification based on adaptive online broad learning system", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280069 (11 October 2023); https://doi.org/10.1117/12.3003781

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