| 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 | 616 | 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 | 530 | 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 | 210 | 2019 |
| River: machine learning for streaming data in python J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ... The Journal of Machine Learning Research 22 (1), 4945-4952, 2021 | 146 | 2021 |
| 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 | 105 | 2017 |
| 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 | 91 | 2019 |
| 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 | 71 | 2021 |
| Adaptive random forests for data stream regression. HM Gomes, JP Barddal, LEB Ferreira, A Bifet ESANN, 2018 | 56 | 2018 |
| 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 |
| 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 | 40 | 2019 |
| 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 | 33 | 2015 |
| 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 | 32 | 2019 |
| 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 | 31 | 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 | 30 | 2017 |
| A survey on feature drift adaptation JP Barddal, HM Gomes, F Enembreck 2015 IEEE 27th International Conference on Tools with Artificial …, 2015 | 29 | 2015 |
| Delayed labelling evaluation for data streams M Grzenda, HM Gomes, A Bifet Data Mining and Knowledge Discovery 34 (5), 1237-1266, 2020 | 28 | 2020 |
| Generating action plans for poultry management using artificial neural networks R Ribeiro, D Casanova, M Teixeira, A Wirth, HM Gomes, AP Borges, ... Computers and Electronics in Agriculture 161, 131-140, 2019 | 28 | 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 | 28 | 2019 |
| SAE2: advances on the social adaptive ensemble classifier for data streams HM Gomes, F Enembreck Proceedings of the 29th annual ACM symposium on applied computing, 798-804, 2014 | 26 | 2014 |
| SFNClassifier: A scale-free social network method to handle concept drift JP Barddal, HM Gomes, F Enembreck Proceedings of the 29th Annual ACM Symposium on Applied Computing, 786-791, 2014 | 26 | 2014 |
Albert BifetTélécom ParisTech & University of WaikatoПотвърден имейл адрес: telecom-paristech.fr
