| 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 | 593 | 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 | 512 | 2017 |
| 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 | 103 | 2017 |
| Trust and reputation models for multiagent systems J Granatyr, V Botelho, OR Lessing, EE Scalabrin, JP Barthès, ... ACM Computing Surveys (CSUR) 48 (2), 1-42, 2015 | 85 | 2015 |
| Distributed constraint optimization problems: Review and perspectives AR Leite, F Enembreck, JPA Barthes Expert Systems with Applications 41 (11), 5139-5157, 2014 | 56 | 2014 |
| A framework for dynamic classifier selection oriented by the classification problem difficulty AL Brun, AS Britto Jr, LS Oliveira, F Enembreck, R Sabourin Pattern Recognition 76, 175-190, 2018 | 48 | 2018 |
| Personal assistant to improve CSCW F Enembreck, JP Barthes The 7th International Conference on Computer Supported Cooperative Work in …, 2002 | 42 | 2002 |
| 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 | 41 | 2016 |
| Lessons learned from data stream classification applied to credit scoring JP Barddal, L Loezer, F Enembreck, R Lanzuolo Expert Systems with Applications 162, 113899, 2020 | 37 | 2020 |
| 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 |
| 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 | 30 | 2019 |
| 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 |
| 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 | 28 | 2017 |
| Web image classification based on the fusion of image and text classifiers P Kalva, F Enembreck, A Koerich Ninth International Conference on Document Analysis and Recognition (ICDAR …, 2007 | 28 | 2007 |
| A survey on feature drift adaptation JP Barddal, HM Gomes, F Enembreck 2015 IEEE 27th International Conference on Tools with Artificial …, 2015 | 27 | 2015 |
| Contribution of data complexity features on dynamic classifier selection AL Brun, AS Britto, LS Oliveira, F Enembreck, R Sabourin 2016 International Joint Conference on Neural Networks (IJCNN), 4396-4403, 2016 | 26 | 2016 |
| 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 |
| 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 |
| Interaction Models for Multiagent Reinforcement Learning R Ribeiro, AP Borges, F Enembreck Computational Intelligence for Modelling Control & Automation, 2008 …, 2008 | 23 | 2008 |
Jean Paul BarddalPrograma de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do ParanáDirección de correo verificada de ppgia.pucpr.br
