| Adaptive random forests for evolving data stream classification HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfharinger, ... Machine Learning 106, 1469-1495, 2017 | 811 | 2017 |
| A survey on ensemble learning for data stream classification HM Gomes, JP Barddal, F Enembreck, A Bifet ACM Computing Surveys (CSUR) 50 (2), 1-36, 2017 | 674 | 2017 |
| Machine learning for streaming data: state of the art, challenges, and opportunities HM Gomes, J Read, A Bifet, JP Barddal, J Gama ACM SIGKDD Explorations Newsletter 21 (2), 6-22, 2019 | 320 | 2019 |
| River: machine learning for streaming data in python J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ... Journal of Machine Learning Research 22 (110), 1-8, 2021 | 300 | 2021 |
| Streaming random patches for evolving data stream classification HM Gomes, J Read, A Bifet 2019 IEEE international conference on data mining (ICDM), 240-249, 2019 | 151 | 2019 |
| A survey on feature drift adaptation: Definition, benchmark, challenges and future directions JP Barddal, HM Gomes, F Enembreck, B Pfahringer Journal of Systems and Software 127, 278-294, 2017 | 131 | 2017 |
| Data stream analysis: Foundations, major tasks and tools M Bahri, A Bifet, J Gama, HM Gomes, S Maniu Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1405, 2021 | 123 | 2021 |
| Adaptive random forests for data stream regression. HM Gomes, JP Barddal, LEB Ferreira, A Bifet ESANN, 2018 | 76 | 2018 |
| Adaptive random forests with resampling for imbalanced data streams LEB Ferreira, HM Gomes, A Bifet, LS Oliveira 2019 International Joint Conference on Neural Networks (IJCNN), 1-6, 2019 | 56 | 2019 |
| A survey on semi-supervised learning for delayed partially labelled data streams HM Gomes, M Grzenda, R Mello, J Read, MH Le Nguyen, A Bifet ACM Computing Surveys 55 (4), 1-42, 2022 | 54 | 2022 |
| Shallow security: On the creation of adversarial variants to evade machine learning-based malware detectors F Ceschin, M Botacin, HM Gomes, LS Oliveira, A Grégio Proceedings of the 3rd Reversing and Offensive-oriented Trends Symposium, 1-9, 2019 | 52 | 2019 |
| C-smote: Continuous synthetic minority oversampling for evolving data streams A Bernardo, HM Gomes, J Montiel, B Pfahringer, A Bifet, E Della Valle 2020 IEEE International Conference on Big Data (Big Data), 483-492, 2020 | 49 | 2020 |
| Delayed labelling evaluation for data streams M Grzenda, HM Gomes, A Bifet Data Mining and Knowledge Discovery 34 (5), 1237-1266, 2020 | 46 | 2020 |
| On dynamic feature weighting for feature drifting data streams JP Barddal, H Murilo Gomes, F Enembreck, B Pfahringer, A Bifet Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 42 | 2016 |
| Boosting decision stumps for dynamic feature selection on data streams JP Barddal, F Enembreck, HM Gomes, A Bifet, B Pfahringer Information Systems 83, 13-29, 2019 | 40 | 2019 |
| Merit-guided dynamic feature selection filter for data streams JP Barddal, F Enembreck, HM Gomes, A Bifet, B Pfahringer Expert Systems with Applications 116, 227-242, 2019 | 40 | 2019 |
| Improving credit risk prediction in online peer-to-peer (p2p) lending using imbalanced learning techniques LEB Ferreira, JP Barddal, HM Gomes, F Enembreck 2017 IEEE 29th International Conference on Tools with Artificial …, 2017 | 40 | 2017 |
| Fast & furious: On the modelling of malware detection as an evolving data stream F Ceschin, M Botacin, HM Gomes, F Pinagé, LS Oliveira, A Grégio Expert Systems with Applications 212, 118590, 2023 | 39 | 2023 |
| SNCStream: A social network-based data stream clustering algorithm JP Barddal, HM Gomes, F Enembreck Proceedings of the 30th annual ACM symposium on applied computing, 935-940, 2015 | 39 | 2015 |
| STUDD: a student–teacher method for unsupervised concept drift detection V Cerqueira, HM Gomes, A Bifet, L Torgo Machine Learning 112 (11), 4351-4378, 2023 | 38 | 2023 |
Albert BifetTélécom ParisTech & University of WaikatoVerified email at telecom-paristech.fr
