A segment-based drift adaptation method for data streams
In concept drift adaptation, we aim to design a blind or an informed strategy to update our
best predictor for future data at each time point. However, existing informed drift adaptation …
best predictor for future data at each time point. However, existing informed drift adaptation …
Learning data streams with changing distributions and temporal dependency
In a data stream, concept drift refers to unpredictable distribution changes over time, which
violates the identical-distribution assumption required by conventional machine learning …
violates the identical-distribution assumption required by conventional machine learning …
A drift region-based data sample filtering method
Concept drift refers to changes in the underlying data distribution of data streams over time.
A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it …
A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it …
Diverse instance-weighting ensemble based on region drift disagreement for concept drift adaptation
Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …
Concept drift adaptation by exploiting drift type
Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …
When this happens, model predictions become less accurate. Hence, models built in the …
A selective transfer learning method for concept drift adaptation
G Xie, Y Sun, M Lin, K Tang - Advances in Neural Networks-ISNN 2017 …, 2017 - Springer
Abstract Concept drift is one of the key challenges that incremental learning needs to deal
with. So far, a lot of algorithms have been proposed to cope with it, but it is still difficult to …
with. So far, a lot of algorithms have been proposed to cope with it, but it is still difficult to …
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation
As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been
tested extensively with static data. However, real-world applications often involve dynamic …
tested extensively with static data. However, real-world applications often involve dynamic …
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams
Existing concept drift adaptation (CDA) methods aim to continually update outdated
classifiers in a single-labeled stream scenario. However, real-world data streams are …
classifiers in a single-labeled stream scenario. However, real-world data streams are …
Concept drift adaptation by exploiting historical knowledge
Incremental learning with concept drift has often been tackled by ensemble methods, where
models built in the past can be retrained to attain new models for the current data. Two …
models built in the past can be retrained to attain new models for the current data. Two …
Concept drift adaptation methods under the deep learning framework: A literature review
Q Xiang, L Zi, X Cong, Y Wang - Applied Sciences, 2023 - mdpi.com
With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …
become an integral part of decision making. At the same time, deep learning is one of the …