Y Song, J Lu, H Lu, G Zhang - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning …
Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it …
A Liu, J Lu, G Zhang - … on neural networks and learning systems, 2020 - ieeexplore.ieee.org
Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …
J Li, H Yu, Z Zhang, X Luo, S Xie - ACM Transactions on Knowledge …, 2024 - dl.acm.org
Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the …
G Xie, Y Sun, M Lin, K Tang - Advances in Neural Networks-ISNN 2017 …, 2017 - Springer
Abstract Concept drift is one of the key challenges that incremental learning needs to deal with. So far, a lot of algorithms have been proposed to cope with it, but it is still difficult to …
As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been tested extensively with static data. However, real-world applications often involve dynamic …
E Yu, Y Song, G Zhang, J Lu - Neurocomputing, 2022 - Elsevier
Existing concept drift adaptation (CDA) methods aim to continually update outdated classifiers in a single-labeled stream scenario. However, real-world data streams are …
Y Sun, K Tang, Z Zhu, X Yao - IEEE transactions on neural …, 2018 - ieeexplore.ieee.org
Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be retrained to attain new models for the current data. Two …
Q Xiang, L Zi, X Cong, Y Wang - Applied Sciences, 2023 - mdpi.com
With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the …