… adaptive iterations (AdIter) to enhance the gradient boosting method so that it can be compatible
with different drift … context of gradient boosting decision tree (GBDT) as an example of a …

… for adapting to sudden drifts or streams where concept drifts … number of iterations. We have
the residual vector of the mth … for concept drift adaptation, we will mainly focus on enhancing …

… of adapting the booster to new concept following a concept drift. … the booster to adapt
dynamically to concept drifts. Unlike … It then employs multiple training iterations via majority vote …

… of classifiers to detect concept drift to adapt to various … once and cannot be used for iterative
model training. As a result, … accurate and robust adaptive models of concept drift to cope …

… Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation.
Neurocomputing 491 (2022), 288ś304. [39] Mingyuan Wang and Adrian Barbu. …

… , the streaming decision tree [15] deals with the concept drift … Adaptive learning algorithms
under concept drift require that … with an increase in the iterations and tends to be stable. It …

… is a gradient boosting framework based on decision trees. It … Since concept drift is often
related to covariate shift and prior … developed, which is called Elastic Weight Integration (EWC). …

… Gradient Boosted Trees (SGBT), a novel gradientboosted … learners, concept drift detection,
and adaptation strategies to … regularization methods: Elastic Weight Consolidation (EWC) […

… drift, we abstract it as concept drift in data stream mining. … Since the base learners of two
parts are the decision tree model… For the sub-model, we initially train a gradient boosting model …

… for adapting to concept drift. To achieve good performance in … matrix is utilized with elastic
weighted consolidation. … applies the IFNB classification algorithm [24] on the decision tree’s …