[HTML][HTML] From concept drift to model degradation: An overview on performance-aware drift detectors
The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …
machine learning (ML) models. Changes in the system on which the ML model has been …
Two-way concept-cognitive learning via concept movement viewpoint
Representation and learning of concepts are critical problems in data science and cognitive
science. However, the existing research about concept learning has one prevalent …
science. However, the existing research about concept learning has one prevalent …
Detecting group concept drift from multiple data streams
Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …
based algorithms, caused by unforeseeable changes in the underlying distribution of data …
[HTML][HTML] DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role
in optimizing energy scheduling and enabling more flexible and intelligent power grid …
in optimizing energy scheduling and enabling more flexible and intelligent power grid …
Todynet: temporal dynamic graph neural network for multivariate time series classification
Multivariate time series classification (MTSC) is a crucial data mining task that can be
effectively tackled using prevalent deep learning technology. However, current methods …
effectively tackled using prevalent deep learning technology. However, current methods …
Concept drift detection delay index
Data streams may encounter data distribution changes, which can significantly impair the
accuracy of models. Concept drift detection tracks data distribution changes and signals …
accuracy of models. Concept drift detection tracks data distribution changes and signals …
[HTML][HTML] QuadCDD: A quadruple-based approach for understanding concept drift in data streams
Abstract Concept drift is a prevalent phenomenon in data streams that necessitates
detection and in-depth understanding, as it signifies that the statistical properties of a target …
detection and in-depth understanding, as it signifies that the statistical properties of a target …
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 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 …
Online boosting adaptive learning under concept drift for multistream classification
Multistream classification poses significant challenges due to the necessity for rapid
adaptation in dynamic streaming processes with concept drift. Despite the growing research …
adaptation in dynamic streaming processes with concept drift. Despite the growing research …