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Analysis of Descriptors of Concept Drift and Their Impacts
InformaticsConcept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies.
Albert Costa +2 more
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Handling concept drift via model reuse [PDF]
Machine Learning, 2019 In many real-world applications, data are often collected in the form of stream, and thus the distribution usually changes in nature, which is referred as concept drift in literature. We propose a novel and effective approach to handle concept drift via model reuse, leveraging previous knowledge by reusing models. Each model is associated with a weight
Peng Zhao, Le-Wen Cai, Zhi-Hua Zhou
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Concept Drift Detection Based on Deep Neural Networks and Autoencoders
Applied SciencesIn domains such as fraud detection, healthcare, and industrial equipment maintenance, streaming data often exhibit characteristics such as continuous generation, high real-time processing requirements, and complex distributions, making it susceptible to ...
Lisha Hu, Yaru Lu, Yuehua Feng
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Research on Drift Calculation of Concept Lattice for Sliding Window Method
Jisuanji kexue yu tansuo, 2021 Concept lattice is an effective tool for data analysis and rule acquisition. In recent years, the application and research of concept lattice has gradually become an important research direction in the field of data analysis.
XU Jilin, XU Jianfeng, LIU Long, WU Fangwen
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Detection & Management of Concept Drift
2006 International Conference on Machine Learning and Cybernetics, 2006 The ability to correctly detect the location and derive the contextual information where a concept begins to drift is essential in the study of domains with changing context. This paper proposes a Top-down learning method with the incorporation of a learning accuracy mechanism to efficiently detect and manage context changes within a large dataset ...
Mak, Lee-Onn, Krause, Paul
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Predictive learning models for concept drift [PDF]
Theoretical Computer Science, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Case, John +4 more
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Unsupervised Tuning for Drift Detectors Using Change Detector Segmentation
IEEE AccessConcept drifts can occur due to various factors such as changes in the environment or sensor degradation, posing significant challenges to machine learning systems by potentially skewing decision-making processes. Therefore, detecting drifts is essential
Ricardo Petri Silva +3 more
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, 2018 One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over
Principe, Jose C. +2 more
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A modified Learn++.NSE algorithm for dealing with concept drift [PDF]
, 2014 © 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Concept drift is a very pervasive phenomenon in real world applications. By virtue of variety change types of concept drift, it makes more difficult for learning algorithm to track ...
Dong, F, Li, K, Lu, J, Zhang, G
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Concept drift detection based on anomaly analysis [PDF]
, 2014 © Springer International Publishing Switzerland 2014. In online machine learning, the ability to adapt to new concept quickly is highly desired. In this paper, we propose a novel concept drift detection method, which is called Anomaly Analysis Drift ...
I. Zliobaite +4 more
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