Zobrazeno 1 - 10
of 19 436
pro vyhledávání: '"concept drift"'
Autor:
Sharief, Farhana1 (AUTHOR) farhana.shareef@uos.edu.pk, Ijaz, Humaira2 (AUTHOR) humaira.bilalrasul@uos.edu.pk, Shojafar, Mohammad3 (AUTHOR) m.shojafar@surrey.ac.uk, Naeem, Muhammad Asif4 (AUTHOR) asif.naeem@nu.edu.pk
Publikováno v:
ACM Computing Surveys. Jan2025, Vol. 57 Issue 1, p1-48. 48p.
Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concep
Externí odkaz:
http://arxiv.org/abs/2410.18478
Dynamic concepts in time series are crucial for understanding complex systems such as financial markets, healthcare, and online activity logs. These concepts help reveal structures and behaviors in sequential data for better decision-making and forec
Externí odkaz:
http://arxiv.org/abs/2410.10041
This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME), a novel solution capable of detecting and mitigating concept drift in Federated Learning (FL) Internet of Things (IoT) environments. Concept drift poses signific
Externí odkaz:
http://arxiv.org/abs/2410.01386
Concept Drift is a phenomenon in which the underlying data distribution and statistical properties of a target domain change over time, leading to a degradation of the model's performance. Consequently, models deployed in production require continuou
Externí odkaz:
http://arxiv.org/abs/2406.17813
Autor:
Galmeanu, Honorius, Andonie, Razvan
In machine learning, concept drift is an evolution of information that invalidates the current data model. It happens when the statistical properties of the input data change over time in unforeseen ways. Concept drift detection is crucial when deali
Externí odkaz:
http://arxiv.org/abs/2406.13754
Autor:
Tveten, Martin, Stakkeland, Morten
Machine learning and statistical methods can be used to enhance monitoring and fault prediction in marine systems. These methods rely on a dataset with records of historical system behaviour, potentially containing periods of both fault-free and faul
Externí odkaz:
http://arxiv.org/abs/2406.08030
Autor:
Varshney, Ayush K., Torra, Vicenc
Deep neural networks (DNNs) are one of the most widely used machine learning algorithm. DNNs requires the training data to be available beforehand with true labels. This is not feasible for many real-world problems where data arrives in the streaming
Externí odkaz:
http://arxiv.org/abs/2406.04903
Autor:
Lalletti, Cristiana, Teso, Stefano
Long-running machine learning models face the issue of concept drift (CD), whereby the data distribution changes over time, compromising prediction performance. Updating the model requires detecting drift by monitoring the data and/or the model for u
Externí odkaz:
http://arxiv.org/abs/2407.16515
This study builds upon our previous work by introducing a refined Inductive Conformal Martingale (ICM) approach for addressing Concept Drift (CD). Specifically, we enhance our previously proposed CAUTIOUS betting function to incorporate multiple dens
Externí odkaz:
http://arxiv.org/abs/2406.15760