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 …
[HTML][HTML] A survey on machine learning for recurring concept drifting data streams
AL Suárez-Cetrulo, D Quintana, A Cervantes - Expert Systems with …, 2023 - Elsevier
The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
Deep neural networks and tabular data: A survey
Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …
numerous critical and computationally demanding applications. On homogeneous datasets …
A survey on negative transfer
Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …
facilitate learning in a target domain. It is particularly useful when the target domain has very …
A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection
Today's internet data primarily consists of streamed data from various applications like
sensor networks, banking data and telecommunication data networks. A new field of study …
sensor networks, banking data and telecommunication data networks. A new field of study …
IoT data analytics in dynamic environments: From an automated machine learning perspective
With the wide spread of sensors and smart devices in recent years, the data generation
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …
Memory-efficient class-incremental learning for image classification
With the memory-resource-limited constraints, class-incremental learning (CIL) usually
suffers from the “catastrophic forgetting” problem when updating the joint classification …
suffers from the “catastrophic forgetting” problem when updating the joint classification …
Selective ensemble-based online adaptive deep neural networks for streaming data with concept drift
H Guo, S Zhang, W Wang - Neural Networks, 2021 - Elsevier
Abstract Concept drift is an important issue in the field of streaming data mining. However,
how to maintain real-time model convergence in a dynamic environment is an important and …
how to maintain real-time model convergence in a dynamic environment is an important and …
A survey on unbalanced classification: How can evolutionary computation help?
Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …
widespread attention from both the academic and industrial communities due mainly to its …
Concept drift type identification based on multi-sliding windows
H Guo, H Li, Q Ren, W Wang - Information Sciences, 2022 - Elsevier
Abstract Concept drift is a common and important issue in streaming data analysis and
mining. Thus far, many concept drift detection methods have been proposed but may not be …
mining. Thus far, many concept drift detection methods have been proposed but may not be …