Dynamic ensemble selection for imbalanced data streams with concept drift
B Jiao, Y Guo, D Gong, Q Chen - IEEE transactions on neural …, 2022 - ieeexplore.ieee.org
Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …
combination of base classifiers according to their global performances. However, concept …
Nonstationary data stream classification with online active learning and siamese neural networks✩
We have witnessed in recent years an ever-growing volume of information becoming
available in a streaming manner in various application areas. As a result, there is an …
available in a streaming manner in various application areas. As a result, there is an …
Cost-sensitive continuous ensemble kernel learning for imbalanced data streams with concept drift
Y Chen, X Yang, HL Dai - Knowledge-Based Systems, 2024 - Elsevier
In stream learning, data continuously arrives over time, often at a very high rate. For
imbalanced data streams with concept drift, it becomes essential to simultaneously address …
imbalanced data streams with concept drift, it becomes essential to simultaneously address …
Online learning with adaptive rebalancing in nonstationary environments
K Malialis, CG Panayiotou… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
An enormous and ever-growing volume of data is nowadays becoming available in a
sequential fashion in various real-world applications. Learning in nonstationary …
sequential fashion in various real-world applications. Learning in nonstationary …
Data augmentation on-the-fly and active learning in data stream classification
K Malialis, D Papatheodoulou… - 2022 IEEE …, 2022 - ieeexplore.ieee.org
There is an emerging need for predictive models to be trained on-the-fly, since in numerous
machine learning applications data are arriving in an online fashion. A critical challenge …
machine learning applications data are arriving in an online fashion. A critical challenge …
Autoencoder-based anomaly detection in streaming data with incremental learning and concept drift adaptation
J Li, K Malialis, MM Polycarpou - 2023 International Joint …, 2023 - ieeexplore.ieee.org
In our digital universe nowadays, enormous amount of data are produced in a streaming
manner in a variety of application areas. These data are often unlabelled. In this case …
manner in a variety of application areas. These data are often unlabelled. In this case …
DynaQ: online learning from imbalanced multi-class streams through dynamic sampling
Online supervised learning from fast-evolving data streams, particularly in domains such as
health, the environment, and manufacturing, is a crucial research area. However, these …
health, the environment, and manufacturing, is a crucial research area. However, these …
Online-MC-queue: Learning from imbalanced multi-class streams
Online supervised learning from fast-evolving data streams has application in many areas.
The development of techniques with highly skewed class distributions (or'class imbalance') …
The development of techniques with highly skewed class distributions (or'class imbalance') …
A hybrid active-passive approach to imbalanced nonstationary data stream classification
In real-world applications, the process generating the data might suffer from nonstationary
effects (eg, due to seasonality, faults affecting sensors or actuators, and changes in the …
effects (eg, due to seasonality, faults affecting sensors or actuators, and changes in the …
Unsupervised unlearning of concept drift with autoencoders
Concept drift refers to a change in the data distribution affecting the data stream of future
samples. Consequently, learning models operating on the data stream might become …
samples. Consequently, learning models operating on the data stream might become …