| 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 | 1704 | 2018 |
| 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 | 135 | 2018 |
| 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 | 131 | 2017 |
| 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 | 95 | 2020 |
| 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 | 94 | 2017 |
| 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 | 92 | 2021 |
| 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 | 78 | 2020 |
| 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 | 67 | 2020 |
| Learning bounds for open-set learning Z Fang, J Lu, A Liu, F Liu, G Zhang International conference on machine learning, 3122-3132, 2021 | 63 | 2021 |
| 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 | 36 | 2022 |
| 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 33 (9), 4876-4889, 2021 | 33 | 2021 |
| Concept drift detection delay index A Liu, J Lu, Y Song, J Xuan, G Zhang IEEE Transactions on Knowledge and Data Engineering 35 (5), 4585-4597, 2022 | 26 | 2022 |
| 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 23 (9), 15155-15165, 2022 | 26 | 2022 |
| 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 | 22 | 2020 |
| 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 53 (4), 2110-2123, 2021 | 21 | 2021 |
| 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 | 12 | 2020 |
| 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 AI 2020: Advances in Artificial Intelligence: 33rd Australasian Joint …, 2020 | 9 | 2020 |
| Concept drift detection based on anomaly analysis A Liu, G Zhang, J Lu Neural Information Processing: 21st International Conference, ICONIP 2014 …, 2014 | 9 | 2014 |
| An empirical study of fuzzy decision tree for gradient boosting ensemble Z Liu, A Liu, G Zhang, J Lu Australasian Joint Conference on Artificial Intelligence, 716-727, 2022 | 5 | 2022 |
| 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 | 4 | 2019 |
Jie LU AO, FIEEE, FIFSA, FACS, Austr...Distinguished Professor, Director of Australian Artificial Intelligence InstituteVerified email at uts.edu.au
