Results 11 to 20 of about 5,397,718 (323)
An Overview on Concept Drift Learning [PDF]
IEEE Access, 2019 Concept drift techniques aim at learning patterns from data streams that may change over time. Although such behavior is not usually expected in controlled environments, real-world scenarios can face changes in the data, such as new classes, clusters ...
Adriana Sayuri Iwashita, Joao Paulo Papa
doaj +4 more sources
Concept Drift Detection Delay Index
IEEE Transactions on Knowledge and Data Engineering, 2023 Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models.
Anjin Liu +4 more
semanticscholar +2 more sources
LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing [PDF]
PeerJ Computer ScienceThis study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution.
Tajwar Mehmood +4 more
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Survey of Concept Drift Handling Methods in Data Streams [PDF]
Jisuanji kexue, 2022 At present,concept drift in the nonstationary data stream presents a trend of different speeds and and different space distribution,which has brought great challenges to many fields such as data mining and machine learning.In the past two de-cades,many ...
CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long
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From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors [PDF]
Knowledge-Based Systems, 2022 The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system's life cycle ...
Firas Bayram +2 more
semanticscholar +1 more source
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling [PDF]
Neural Information Processing Systems, 2023 Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms are designed for online time series forecasting, with some exploiting cross-
Yifan Zhang +8 more
semanticscholar +1 more source
Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions [PDF]
Transactions on Fuzzy Sets and Systems, 2022 People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been
Tzung-Pei Hong +3 more
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Characterizing concept drift [PDF]
Data Mining and Knowledge Discovery, 2016 Accepted for publication in Data Mining and Knowledge ...
Geoffrey I. Webb +4 more
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Learning under Concept Drift: A Review [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2019 Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation.
Jie Lu +5 more
semanticscholar +1 more source
Federated Learning under Distributed Concept Drift [PDF]
International Conference on Artificial Intelligence and Statistics, 2022 Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for FL, because drifts arise staggered in time and space (across clients). To
Ellango Jothimurugesan +4 more
semanticscholar +1 more source