A survey on concept drift adaptation

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Items 401–500 of 1,007

1. Learning from Imbalanced Data Streams
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2. SACCOS: A Semi-Supervised Framework for Emerging Class Detection and Concept Drift Adaption Over Data Streams
   Yang Gao, Swarup Chandra, Yifan Li, Latifur R. Khan, Thuraisingham Bhavani · 2020 · IEEE Transactions on Knowledge and Data Engineering
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7. A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams
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9. Online AdaBoost-based methods for multiclass problems
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12. Self-Adaptive Windowing Approach for Handling Complex Concept Drift
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16. SHIELD
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17. Exploiting concept drift to predict popularity of social multimedia in microblogs
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18. Batch-based active learning: Application to social media data for crisis management
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19. Concept drift in e-mail datasets: An empirical study with practical implications
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20. Exploring complex and big data
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21. Combining active learning with concept drift detection for data stream mining
    Bartosz Krawczyk, Bernhard Pfahringer, Michał Woźniak · 2018 · IEEE International Conference on Big Data (BigData)
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23. Time-aware Concept Drift Detection Using the Earth Mover’s Distance
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24. Tiny Machine Learning for Concept Drift
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25. Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm
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27. An effectiveness analysis of transfer learning for the concept drift problem in malware detection
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29. Model Drift in Dynamic Networks
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30. A comprehensive analysis of concept drift locality in data streams
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34. Diagnosing Concept Drift with Visual Analytics
    Weikai Yang, Zhen Li, Mengchen Liu, Yafeng Lu, Kelei Cao, Ross Maciejewski, Shixia Liu · 2020 · IEEE Conference on Visual Analytics Science and Technology (VAST)
35. ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data
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37. Learning Data Streams With Changing Distributions and Temporal Dependency
    Yiliao Song, Jie Lu B, Haiyan Lu, Guangquan Zhang · 2021 · IEEE Transactions on Neural Networks and Learning Systems
38. A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques
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40. Unsupervised Concept Drift Detection From Deep Learning Representations in Real-Time
    Salvatore Greco, Bartolomeo Vacchetti, Daniele Apiletti, Tania Cerquitelli · 2025 · IEEE Transactions on Knowledge and Data Engineering
41. Improving the accuracy of long-term travel time prediction using heterogeneous ensembles
    João Mendes‐Moreira, Alí­pio Jorge, Jorge Freire de Sousa, Carlos Soares · 2014 · Neurocomputing
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48. Adaptive Federated Learning in Presence of Concept Drift
    Giuseppe Canonaco, Alex Bergamasco, Alessio Mongelluzzo, Manuel Roveri · 2021 · International Joint Conference on Neural Networks (IJCNN)
49. Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques
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50. FedStream: Prototype-Based Federated Learning on Distributed Concept-Drifting Data Streams
    Cobbinah B. Mawuli, Liwei Che, Jay Kumar, Salah Ud Din, Zhili Qin, Qinli Yang, Junming Shao · 2023 · IEEE Transactions on Systems Man and Cybernetics Systems
51. Probabilistic graphical models in modern social network analysis
    Alireza Farasat, Alexander Nikolaev, Sargur N. Srihari, Rachael Hageman Blair · 2015 · Social Network Analysis and Mining
52. Learning Decision Trees from Data Streams with Concept Drift
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53. Combining univariate approaches for ensemble change detection in multivariate data
    William J. Faithfull, Juan J. Rodríguez, Ludmila Ilieva Kuncheva · 2018 · Information Fusion
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    Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer · 2018 · Expert Systems with Applications
55. Outlier and anomaly pattern detection on data streams
    Cheong Hee Park · 2018 · The Journal of Supercomputing
56. Kraken: Memory-Efficient Continual Learning for Large-Scale Real-Time Recommendations
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57. Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams
    Martha Roseberry, Bartosz Krawczyk, Youcef Djenouri, Alberto Cano · 2021 · Neurocomputing
58. A Segment-Based Drift Adaptation Method for Data Streams
    Yiliao Song, Jie Lu B, Anjin Liu, Haiyan Lu, Guangquan Zhang · 2021 · IEEE Transactions on Neural Networks and Learning Systems
59. Online Learning in Variable Feature Spaces under Incomplete Supervision
    Yi He, Xu Yuan, Sheng Chen, Xindong Wu · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
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65. Concept Drift Adaptation by Exploiting Drift Type
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