[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 …
A survey on data preprocessing for data stream mining: Current status and future directions
Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …
discovery scenarios, dominated by increasingly large datasets. These methods aim at …
Discussion and review on evolving data streams and concept drift adapting
I Khamassi, M Sayed-Mouchaweh, M Hammami… - Evolving systems, 2018 - Springer
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 …
An overview on concept drift learning
AS Iwashita, JP Papa - IEEE access, 2018 - ieeexplore.ieee.org
Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …
time. Although such behavior is not usually expected in controlled environments, real-world …
No free lunch theorem for concept drift detection in streaming data classification: A review
H Hu, M Kantardzic, TS Sethi - Wiley Interdisciplinary Reviews …, 2020 - Wiley Online Library
Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …
are unlabeled because the sheer volume of the stream makes it impractical to label a …
[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 …
Evolving spiking neural networks for online learning over drifting data streams
Nowadays huge volumes of data are produced in the form of fast streams, which are further
affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of …
affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of …
A survey of active and passive concept drift handling methods
M Han, Z Chen, M Li, H Wu… - Computational …, 2022 - Wiley Online Library
At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …
speeds and different degrees of severity, which has brought great challenges to many fields …
[PDF][PDF] Survey on Online Streaming Continual Learning.
Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning
algorithm should adapt to input data distribution shifts without sacrificing accuracy. These …
algorithm should adapt to input data distribution shifts without sacrificing accuracy. These …
Concept drift modeling for robust autonomous vehicle control systems in time-varying traffic environments
S Lee, SH Park - Expert Systems with Applications, 2022 - Elsevier
Autonomous vehicle systems (AVSs) are widely used to transfer wafers in semiconductor
manufacturing. However, in such systems, robust traffic control is a significant challenge …
manufacturing. However, in such systems, robust traffic control is a significant challenge …