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Jean Paul Barddal
Jean Paul Barddal
Programa de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná
Dirección de correo verificada de ppgia.pucpr.br - Página principal
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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
5212017
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
4522017
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
160*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
942017
Adaptive random forests for data stream regression.
HM Gomes, JP Barddal, LEB Ferreira, A Bifet
ESANN, 2018
442018
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
392016
A survey on concept drift in process mining
DMV Sato, SC De Freitas, JP Barddal, EE Scalabrin
ACM Computing Surveys (CSUR) 54 (9), 1-38, 2021
322021
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
312020
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
312015
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
262017
A survey on feature drift adaptation
JP Barddal, HM Gomes, F Enembreck
2015 IEEE 27th International Conference on Tools with Artificial …, 2015
262015
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
262014
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
252019
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
242019
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
212016
Analyzing the impact of feature drifts in streaming learning
JP Barddal, HM Gomes, F Enembreck
Neural Information Processing: 22nd International Conference, ICONIP 2015 …, 2015
202015
Cost-sensitive learning for imbalanced data streams
L Loezer, F Enembreck, JP Barddal, A de Souza Britto Jr
Proceedings of the 35th annual ACM symposium on applied computing, 498-504, 2020
132020
A case study of batch and incremental recommender systems in supermarket data under concept drifts and cold start
AD Viniski, JP Barddal, A de Souza Britto Jr, F Enembreck, ...
Expert Systems with Applications 176, 114890, 2021
112021
Learning regularized hoeffding trees from data streams
JP Barddal, F Enembreck
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 574-581, 2019
102019
Hierarchical classification of data streams: a systematic literature review
E Tieppo, RR Santos, JP Barddal, JC Nievola
Artificial Intelligence Review, 1-40, 2022
92022
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