[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 …
[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 …
Discussion and review on evolving data streams and concept drift adapting
Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …
problems related to the dynamicity of the environments. In increasing number of real world …
Online active learning for drifting data streams
Classification methods for streaming data are not new, but very few current frameworks
address all three of the most common problems with these tasks: concept drift, noise, and …
address all three of the most common problems with these tasks: concept drift, noise, and …
Online active learning ensemble framework for drifted data streams
J Shan, H Zhang, W Liu, Q Liu - IEEE transactions on neural …, 2018 - ieeexplore.ieee.org
In practical applications, data stream classification faces significant challenges, such as high
cost of labeling instances and potential concept drifting. We present a new online active …
cost of labeling instances and potential concept drifting. We present a new online active …
DetectA: abrupt concept drift detection in non-stationary environments
Almost all drift detection mechanisms designed for classification problems work reactively:
after receiving the complete data set (input patterns and class labels) they apply a sequence …
after receiving the complete data set (input patterns and class labels) they apply a sequence …
A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams
Nowadays the amount of networks of devices and sensors, such as smart homes or smart
cities, is rapidly increasing. Each of these devices generates massive amounts of data on a …
cities, is rapidly increasing. Each of these devices generates massive amounts of data on a …
Self-adaptive windowing approach for handling complex concept drift
Detecting changes in data streams attracts major attention in cognitive computing systems.
The challenging issue is how to monitor and detect these changes in order to preserve the …
The challenging issue is how to monitor and detect these changes in order to preserve the …
On-line self-adaptive framework for tailoring a neural-agent learning model addressing dynamic real-time scheduling problems
Z Hammami, W Mouelhi, LB Said - Journal of Manufacturing Systems, 2017 - Elsevier
The dynamic nature and time-varying behavior of actual environments provide serious
challenges for learning models. Thus, changes may deteriorate the constructed control …
challenges for learning models. Thus, changes may deteriorate the constructed control …
Fepds: A proposal for the extraction of fuzzy emerging patterns in data streams
Nowadays, most data is generated by devices that produce data continuously. These kinds
of data can be categorized as data streams and valuable insights can be extracted from …
of data can be categorized as data streams and valuable insights can be extracted from …