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Anjin Liu
Anjin Liu
Decision Systems & E-Service Intelligence Research Laboratory, AAII, University of Technology Sydney
Bestätigte E-Mail-Adresse bei uts.edu.au
Titel
Zitiert von
Zitiert von
Jahr
Learning under concept drift: A review
J Lu, A Liu, F Dong, F Gu, J Gama, G Zhang
IEEE Transactions on Knowledge and Data Engineering 31 (12), 2346-2363, 2018
6072018
Accumulating regional density dissimilarity for concept drift detection in data streams
A Liu, J Lu, F Liu, G Zhang
Pattern Recognition 76, 256-272, 2018
692018
Regional Concept Drift Detection and Density Synchronized Drift Adaptation
A Liu, Y Song, G Zhang, J Lu
Proceedings of the Twenty-sixth International Joint Conference on Artificial …, 2017
652017
Fuzzy time windowing for gradual concept drift adaptation
A Liu, G Zhang, J Lu
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6, 2017
512017
Data-driven decision support under concept drift in streamed big data
J Lu, A Liu, Y Song, G Zhang
Complex & Intelligent Systems 6 (1), 157-163, 2020
432020
Concept drift detection via equal intensity k-means space partitioning
A Liu, J Lu, G Zhang
IEEE transactions on cybernetics 51 (6), 3198-3211, 2020
362020
Diverse instance-weighting ensemble based on region drift disagreement for concept drift adaptation
A Liu, J Lu, G Zhang
IEEE transactions on neural networks and learning systems 32 (1), 293-307, 2020
252020
Learning bounds for open-set learning
Z Fang, J Lu, A Liu, F Liu, G Zhang
International Conference on Machine Learning, 3122-3132, 2021
232021
Confident anchor-induced multi-source free domain adaptation
J Dong, Z Fang, A Liu, G Sun, T Liu
Advances in Neural Information Processing Systems 34, 2848-2860, 2021
142021
A segment-based drift adaptation method for data streams
Y Song, J Lu, A Liu, H Lu, G Zhang
IEEE transactions on neural networks and learning systems, 2021
92021
Concept drift detection based on anomaly analysis
A Liu, G Zhang, J Lu
International Conference on Neural Information Processing, 263-270, 2014
82014
Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning
H Yu, J Lu, A Liu, B Wang, R Li, G Zhang
IEEE Transactions on Intelligent Transportation Systems, 2022
62022
Evolving gradient boost: a pruning scheme based on loss improvement ratio for learning under concept drift
K Wang, J Lu, A Liu, G Zhang, L Xiong
IEEE Transactions on Cybernetics, 2021
52021
Concept drift detection: dealing with missing values via fuzzy distance estimations
A Liu, J Lu, G Zhang
IEEE Transactions on Fuzzy Systems 29 (11), 3219-3233, 2020
52020
Fast switch naïve bayes to avoid redundant update for concept drift learning
A Liu, G Zhang, K Wang, J Lu
2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020
42020
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation
K Wang, J Lu, A Liu, Y Song, L Xiong, G Zhang
Neurocomputing 491, 288-304, 2022
32022
Real-time decision making for train carriage load prediction via multi-stream learning
H Yu, A Liu, B Wang, R Li, G Zhang, J Lu
Australasian Joint Conference on Artificial Intelligence, 29-41, 2020
32020
Concept drift adaptation for learning with streaming data
A Liu
32018
A novel weighting method for online ensemble learning with the presence of concept drift
A LIU, G ZHANG, JIE LU
Decision Making and Soft Computing: Proceedings of the 11th International …, 2014
32014
Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting
D Chow, A Liu, G Zhang, J Lu
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
22019
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