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The document presents Streaming Gradient Boosted Trees (Sgbt), a novel approach to gradient boosting for evolving data streams that addresses challenges associated with concept drift. Sgbt utilizes a Tree Replacement mechanism to dynamically adapt to changes in data distribution without significant performance loss, outperforming existing state-of-the-art methods. Empirical evaluations demonstrate Sgbt

Author

keilabar

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EN