Results 271 to 280 of about 5,397,718 (323)

One or Two Things We know about Concept Drift - A Survey on Monitoring Evolving Environments

arXiv.org, 2023
The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes.
Fabian Hinder, Valerie Vaquet, Barbara Hammer   +2 more
semanticscholar   +1 more source

Localization of Concept Drift: Identifying the Drifting Datapoints

2022 International Joint Conference on Neural Networks (IJCNN), 2022
The notion of concept drift refers to the phenomenon that the distribution which is underlying the observed data changes over time. As a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift, to find change points in data streams, or to adjust models in the presence of ...
Hinder, Fabian, Vaquet, Valerie, Brinkrolf, Johannes, Artelt, André, Hammer, Barbara   +4 more
openaire   +3 more sources

Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift

IEEE Transactions on Neural Networks and Learning Systems, 2022
Ensemble learning, as a popular method to tackle concept drift in data stream, forms a combination of base classifiers according to their global performances.
Botao Jiao, Yinan Guo, Dunwei Gong, Qiuju Chen   +3 more
semanticscholar   +1 more source

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection

AISec@CCS, 2021
Despite decades of research in network traffic analysis and incredible advances in artificial intelligence, network intrusion detection systems based on machine learning (ML) have yet to prove their worth.
Giuseppina Andresini, Feargus Pendlebury, Fabio Pierazzi, Corrado Loglisci, A. Appice, L. Cavallaro   +5 more
semanticscholar   +1 more source

Unsupervised Concept Drift Detection From Deep Learning Representations in Real-Time

IEEE Transactions on Knowledge and Data Engineering
Concept drift is the phenomenon in which the underlying data distributions and statistical properties of a target domain change over time, leading to a degradation in model performance.
Salvatore Greco, Bartolomeo Vacchetti, D. Apiletti, Tania Cerquitelli   +3 more
semanticscholar   +1 more source

Incremental Weighted Ensemble for Data Streams With Concept Drift

IEEE Transactions on Artificial Intelligence
As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical classifier based on an entire chunk ...
Botao Jiao, Yinan Guo, Cuie Yang, Jiayang Pu, Zhiji Zheng, Dunwei Gong   +5 more
semanticscholar   +1 more source

Type-LDD: A Type-Driven Lite Concept Drift Detector for Data Streams

IEEE Transactions on Knowledge and Data Engineering
Concept drift is a phenomenon that the distribution of data streams changes with time. When this happens, model predictions become less accurate. Hence, concept drift needs to be detected and adapted.
Hang Yu, Jinpeng Li, Jie Lu, Yiliao Song, Shaorong Xie, Guangquan Zhang   +5 more
semanticscholar   +1 more source

Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

Knowledge Discovery and Data Mining
Time series forecasting always faces the challenge of concept drift, where data distributions evolve over time, leading to a decline in forecast model performance.
Lifan Zhao, Yanyan Shen
semanticscholar   +1 more source

Concept Drift Visualization

Journal of Information and Computational Science, 2013
Mining data stream are facing many challenges now, one of them is concept drift problem. In many practical applications, concept drift usually affects the classification performance for data stream, or even make the classifier failed. However, most of the proposed methods are mainly focusing on solving concept drift from the data value point of view ...
openaire   +1 more source

A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques

WIREs Data. Mining. Knowl. Discov.
Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas.
Shruti Arora, Rinkle Rani, Nitin Saxena
semanticscholar   +1 more source