Extremely Fast Hoeffding Adaptive Tree

Manapragada, Chaitanya; Salehi, Mahsa; Webb, Geoffrey I.

doi:10.1109/icdm54844.2022.00042

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“…5) The Hoeffding Tree (HT) algorithm, proposed by Domingos et al [14], is a fast and accurate incremental learning algorithm used to handle streaming data or large-scale datasets.…”

Section: Comparison Algorithmmentioning

confidence: 99%

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

Sun,

Guo

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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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.

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“…5) The Hoeffding Tree (HT) algorithm, proposed by Domingos et al [14], is a fast and accurate incremental learning algorithm used to handle streaming data or large-scale datasets.…”

Section: Comparison Algorithmmentioning

confidence: 99%