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[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 …
Learning under concept drift: A review
Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …
data overtime. Concept drift research involves the development of methodologies and …
An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators
Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly
accomplished by the kinematic model establishing the relationship of an anthropomorphic …
accomplished by the kinematic model establishing the relationship of an anthropomorphic …
Fault management in DC microgrids: A review of challenges, countermeasures, and future research trends
The significant benefits of DC microgrids have instigated extensive efforts to be an
alternative network as compared to conventional AC power networks. Although their …
alternative network as compared to conventional AC power networks. Although their …
Data stream mining: methods and challenges for handling concept drift
Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …
research has gained extensive attention over the past decade. However, there are several …
Learning to classify with incremental new class
New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …
data mining. In open incremental data environments, data often come with novel classes, eg …
[HTML][HTML] Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection …
Abstract Intrusion Detection Systems (IDS) have become pivotal in safeguarding information
systems against evolving threats. Concurrently, Concept Drift presents a significant …
systems against evolving threats. Concurrently, Concept Drift presents a significant …
A systematic literature review of novelty detection in data streams: challenges and opportunities
Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …
in streams of data. Many machine learning algorithms have been proposed to detect these …
Accumulating regional density dissimilarity for concept drift detection in data streams
In a non-stationary environment, newly received data may have different knowledge patterns
from the data used to train learning models. As time passes, a learning model's performance …
from the data used to train learning models. As time passes, a learning model's performance …
Robust and rapid adaption for concept drift in software system anomaly detection
Anomaly detection is critical for web-based software systems. Anecdotal evidence suggests
that in these systems, the accuracy of a static anomaly detection method that was previously …
that in these systems, the accuracy of a static anomaly detection method that was previously …