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 …
Learning under concept drift for regression—a systematic literature review
Context: The amount and diversity of data have increased drastically in recent years.
However, in certain situations, the data to which a trained Machine Learning model is …
However, in certain situations, the data to which a trained Machine Learning model is …
Topology learning-based fuzzy random neural networks for streaming data regression
As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure
inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN …
inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN …
Learning data streams with changing distributions and temporal dependency
In a data stream, concept drift refers to unpredictable distribution changes over time, which
violates the identical-distribution assumption required by conventional machine learning …
violates the identical-distribution assumption required by conventional machine learning …
Fuzzy clustering-based adaptive regression for drifting data streams
Current models and algorithms have been increasingly required to learn in a nonstationary
environment because the phenomenon of concept drift (or pattern shift) may occur, that is …
environment because the phenomenon of concept drift (or pattern shift) may occur, that is …
[HTML][HTML] Detecting virtual concept drift of regressors without ground truth values
E Oikarinen, H Tiittanen, A Henelius… - Data Mining and …, 2021 - Springer
Regression analysis is a standard supervised machine learning method used to model an
outcome variable in terms of a set of predictor variables. In most real-world applications the …
outcome variable in terms of a set of predictor variables. In most real-world applications the …
Continuous support vector regression for nonstationary streaming data
Quadratic programming is the process of solving a special type of mathematical optimization
problem. Recent advances in online solutions for quadratic programming problems (QPPs) …
problem. Recent advances in online solutions for quadratic programming problems (QPPs) …
[HTML][HTML] Financial time series forecasting: a data stream mining-based system
Z Bousbaa, J Sanchez-Medina, O Bencharef - Electronics, 2023 - mdpi.com
Data stream mining (DSM) represents a promising process to forecast financial time series
exchange rate. Financial historical data generate several types of cyclical patterns that …
exchange rate. Financial historical data generate several types of cyclical patterns that …
New diversity measure for data stream classification ensembles
K Jackowski - Engineering Applications of Artificial Intelligence, 2018 - Elsevier
The diversity of a voting committee is one of the key characteristics of ensemble systems. It
determines the benefits that can be obtained through classifier fusion. There are many …
determines the benefits that can be obtained through classifier fusion. There are many …
Anti-Forgetting Incremental Learning Algorithm for Interval Type-2 Fuzzy Neural Network
C Sun, H Han, X Wu, H Yang - IEEE Transactions on Fuzzy …, 2023 - ieeexplore.ieee.org
Sample property drift is an essential issue for interval type-2 fuzzy neural networks
(IT2FNNs). When the samples with fresh properties appear, IT2FNN invariably suffers from …
(IT2FNNs). When the samples with fresh properties appear, IT2FNN invariably suffers from …