Jean Paul Barddal
Jean Paul Barddal
Programa de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná
Verified email at ppgia.pucpr.br - Homepage
Title
Cited by
Cited by
Year
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
1842017
Adaptive random forests for evolving data stream classification
HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfharinger, ...
Machine Learning 106 (9-10), 1469-1495, 2017
1552017
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
442017
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
222015
On dynamic feature weighting for feature drifting data streams
JP Barddal, HM Gomes, F Enembreck, B Pfahringer, A Bifet
Joint european conference on machine learning and knowledge discovery in …, 2016
212016
A survey on feature drift adaptation
JP Barddal, HM Gomes, F Enembreck
2015 IEEE 27th International Conference on Tools with Artificial …, 2015
182015
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
182014
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
142016
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
102019
Analyzing the impact of feature drifts in streaming learning
JP Barddal, HM Gomes, F Enembreck
International Conference on Neural Information Processing, 21-28, 2015
102015
Adaptive random forests for data stream regression.
HM Gomes, JP Barddal, LEB Ferreira, A Bifet
ESANN, 2018
92018
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
92017
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
62019
Iterative subset selection for feature drifting data streams
L Yuan, B Pfahringer, JP Barddal
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 510-517, 2018
52018
Pairwise combination of classifiers for ensemble learning on data streams
HM Gomes, JP Barddal, F Enembreck
Proceedings of the 30th Annual ACM Symposium on Applied Computing, 941-946, 2015
52015
Advances on concept drift detection in regression tasks using social networks theory
JP Barddal, HM Gomes, F Enembreck
International Journal of Natural Computing Research (IJNCR) 5 (1), 26-41, 2015
32015
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
22019
Correction to: Adaptive random forests for evolving data stream classification
HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfahringer, ...
Machine Learning 108 (10), 1877-1878, 2019
22019
Vertical and Horizontal Partitioning in Data Stream Regression Ensembles
JP Barddal
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
22019
Learning regularized hoeffding trees from data streams
JP Barddal, F Enembreck
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 574-581, 2019
22019
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