| Analyzing and repairing concept drift adaptation in data stream classification B Halstead, YS Koh, P Riddle, R Pears, M Pechenizkiy, A Bifet, G Olivares, ... Machine Learning 111 (10), 3489-3523, 2022 | 20 | 2022 |
| Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams B Halstead, YS Koh, P Riddle, M Pechenizkiy, A Bifet ACM Transactions on Knowledge Discovery from Data 17 (8), 1-36, 2023 | 9 | 2023 |
| Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency B Halstead, YS Koh, P Riddle, R Pears, M Pechenizkiy, A Bifet Data Mining and Knowledge Discovery 35 (3), 796-836, 2021 | 7 | 2021 |
| Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information B Halstead, YS Koh, P Riddle, M Pechenizkiy, A Bifet, R Pears 2021 IEEE 37th International Conference on Data Engineering (ICDE), 1056-1067, 2021 | 7 | 2021 |
| A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams B Halstead, YS Koh, P Riddle, M Pechenizkiy, A Bifet 2022 IEEE 9th International Conference on Data Science and Advanced …, 2022 | 1 | 2022 |
| FALL: A Modular Adaptive Learning Platform for Streaming Data B Halstead, YS Koh, P Riddle, M Pechenizkiy, A Bifet 2023 IEEE 39th International Conference on Data Engineering (ICDE), 3619-3622, 2023 | | 2023 |
| Measuring Difficulty of Learning Using Ensemble Methods B Chen, YS Koh, B Halstead Australasian Conference on Data Mining, 28-42, 2022 | | 2022 |
| Active Learning Using Difficult Instances B Chen, YS Koh, B Halstead Australasian Joint Conference on Artificial Intelligence, 747-760, 2022 | | 2022 |
