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 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 …
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A well-trained model will be outdated if concept drift occurs.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A well-trained model will be outdated if concept drift occurs.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 …
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 …
Multi-stream concept drift self-adaptation using graph neural network
Concept drift is the phenomenon where the data distribution in a data stream changes over
time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause …
time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause …
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Existing concept drift adaptation (CDA) methods aim to continually update outdated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 handling: A domain adaptation perspective
M Karimian, H Beigy - Expert Systems with Applications, 2023 - Elsevier
Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …
constraints come into account. Concept drift refers to changes in data distribution over time …
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 …
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 …
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 …