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Heitor Murilo Gomes
Heitor Murilo Gomes
Verified email at vuw.ac.nz - Homepage
Title
Cited by
Cited by
Year
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
4222017
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
3802017
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
1072019
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
832017
River: machine learning for streaming data in Python
J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ...
432021
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
402019
Adaptive random forests for data stream regression.
HM Gomes, JP Barddal, LEB Ferreira, A Bifet
ESANN, 2018
392018
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
342016
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
292015
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
262014
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
252014
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
242019
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
232021
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
232019
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
222017
A survey on feature drift adaptation
JP Barddal, HM Gomes, F Enembreck
2015 IEEE 27th International Conference on Tools with Artificial …, 2015
222015
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
202019
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
202016
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
182019
Analyzing the impact of feature drifts in streaming learning
JP Barddal, HM Gomes, F Enembreck
International Conference on Neural Information Processing, 21-28, 2015
182015
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