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 …
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 …
Y Song, J Lu, A Liu, H Lu… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
In concept drift adaptation, we aim to design a blind or an informed strategy to update our best predictor for future data at each time point. However, existing informed drift adaptation …
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 …
M Zhou, J Lu, Y Song, G Zhang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Concept drift is the phenomenon where the data distribution in a data stream changes over time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause …
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 …
M Karimian, H Beigy - Expert Systems with Applications, 2023 - Elsevier
Data stream prediction is challenging when concepts drift, processing time, and memory constraints come into account. Concept drift refers to changes in data distribution over time …
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 …
Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, eg weather prediction, network …