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Concept Drift Adaptation by Exploiting Drift Type
ACM Transactions on Knowledge Discovery from DataConcept 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 past need to be re-learned for the current data. Two design questions need to be
Jinpeng Li +4 more
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ADTCD: An Adaptive Anomaly Detection Approach Toward Concept Drift in IoT
IEEE Internet of Things Journal, 2023The data collected by sensors is streaming data in the Internet of Things (IoT). Although existing deep-learning-based anomaly detection methods generally perform well on static data, they struggle to respond timely to streaming data after distribution ...
Lijuan Xu +4 more
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Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
AAAI Conference on Artificial Intelligence, 2023Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the ...
Enshui Yu +3 more
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Explainable concept drift in process mining
Information Systems, 2023The execution of processes leaves trails of event data in information systems. These event data are analyzed to generate insights and improvements for the underlying process. However, companies do not execute these processes in a vacuum. The fast pace of
Jan Niklas Adams +3 more
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A comprehensive analysis of concept drift locality in data streams
Knowledge-Based Systems, 2023Adapting to drifting data streams is a significant challenge in online learning. Concept drift must be detected for effective model adaptation to evolving data properties.
G. J. Aguiar, Alberto Cano
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03, 2003
We describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. This technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. We can also observe effects on the relative utilization of
Kevin B. Pratt, Gleb Tschapek
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We describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. This technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. We can also observe effects on the relative utilization of
Kevin B. Pratt, Gleb Tschapek
openaire +1 more source
Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network
IEEE Transactions on Knowledge and Data Engineering, 2023Concept 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 a jam in a certain period, leading to a distribution change in ...
M. Zhou +3 more
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