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Jean Paul Barddal
Jean Paul Barddal
Programa de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR)
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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
8212017
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
6712017
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
3192019
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
1302017
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
1092021
Adaptive random forests for data stream regression.
HM Gomes, JP Barddal, LEB Ferreira, A Bifet
ESANN, 2018
742018
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
512020
An explainable machine learning approach for student dropout prediction
JGC Krüger, A de Souza Britto Jr, JP Barddal
Expert Systems with Applications 233, 120933, 2023
422023
A systematic review on computer vision-based parking lot management applied on public datasets
PRL de Almeida, JH Alves, RS Parpinelli, JP Barddal
Expert Systems with Applications 198, 116731, 2022
422022
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
422016
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
402019
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
402019
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
402017
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
392015
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
342020
A survey on feature drift adaptation
JP Barddal, HM Gomes, F Enembreck
2015 IEEE 27th International Conference on Tools with Artificial …, 2015
332015
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
292014
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
242016
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
232021
Analyzing the impact of feature drifts in streaming learning
JP Barddal, HM Gomes, F Enembreck
Neural Information Processing: 22nd International Conference, ICONIP 2015 …, 2015
232015
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Artículos 1–20