| Adaptive random forests for evolving data stream classification HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfharinger, ... Machine Learning 106 (9), 1469-1495, 2017 | 419 | 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 | 378 | 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 | 106 | 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 | 82 | 2017 |
| River: machine learning for streaming data in Python J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ... | 40 | 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 | 39 | 2019 |
| Adaptive random forests for data stream regression. HM Gomes, JP Barddal, LEB Ferreira, A Bifet ESANN, 2018 | 39 | 2018 |
| On dynamic feature weighting for feature drifting data streams JP Barddal, H Murilo Gomes, F Enembreck, B Pfahringer, A Bifet Joint european conference on machine learning and knowledge discovery in …, 2016 | 34 | 2016 |
| 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 | 29 | 2015 |
| 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 | 25 | 2014 |
| 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 | 23 | 2021 |
| 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 | 23 | 2019 |
| 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 | 22 | 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 | 22 | 2017 |
| A survey on feature drift adaptation JP Barddal, HM Gomes, F Enembreck 2015 IEEE 27th International Conference on Tools with Artificial …, 2015 | 22 | 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 | 20 | 2019 |
| SNCStream+: Extending a high quality true anytime data stream clustering algorithm JP Barddal, HM Gomes, F Enembreck, JP Barthès Information Systems 62, 60-73, 2016 | 20 | 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 | 18 | 2019 |
| Analyzing the impact of feature drifts in streaming learning JP Barddal, HM Gomes, F Enembreck International Conference on Neural Information Processing, 21-28, 2015 | 18 | 2015 |
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