Research Publications for Heitor M Gomes
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Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Boosting decision stumps for dynamic feature selection on data streams. Information Systems, 83, 13-29. doi:10.1016/j.is.2019.02.003
Open Access version at:
https://hdl.handle.net/10289/12538
Other publications by:
BIFET FIGUEROL, Albert C :: GOMES, Heitor M :: PFAHRINGER, Bernhard MGomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfahringer, B., . . . Abdessalem, T. (2019). Correction to: Adaptive random forests for evolving data stream classification (Machine Learning, (2017), 106, 9-10, (1469-1495), 10.1007/s10994-017-5642-8). Machine Learning, 108, 1877-1878. doi:10.1007/s10994-019-05793-3
Other publications by:
BIFET FIGUEROL, Albert C :: GOMES, Heitor M :: PFAHRINGER, Bernhard M :: HOLMES, GeoffreyGomes, H. M., Mello, R. F. D., Pfahringer, B., & Bifet, A. (2019). Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 761-769). doi:10.1109/BigData47090.2019.9006366
Grzenda, M., Gomes, H. M., & Bifet, A. (2019). Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery. doi:10.1007/s10618-019-00654-y
Gomes, H. M., Bifet, A., Fournier-Viger, P., Granatyr, J., & Read, J. (2019). Network of experts: Learning from evolving data streams through network-based ensembles. In Proc International Conference on Neural Information Processing (ICONIP 2019) LNCS 11953 (pp. 704-716). Sydney, Australia: Springer. doi:10.1007/978-3-030-36708-4_58
Gomes, H. M., Read, J., & Bifet, A. (2019). Streaming random patches for evolving data stream classification. In Proceedings - IEEE International Conference on Data Mining, ICDM Vol. 2019-November (pp. 240-249). doi:10.1109/ICDM.2019.00034
Le Nguyen, M. H., Gomes, H. M., & Bifet, A. (2019). Semi-supervised Learning over Streaming Data using MOA. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 553-562). doi:10.1109/BigData47090.2019.9006217
Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Merit-guided dynamic feature selection filter for data streams. Expert Systems with Applications, 116, 227-242. doi:10.1016/j.eswa.2018.09.031
Ferreira, L. E. B., Barddal, J. P., Enembreck, F., & Gomes, H. M. (2018). An experimental perspective on sampling methods for imbalanced learning from financial databases. In Proc 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 6 pages). IEEE. doi:10.1109/ijcnn.2018.8489290
Other publications by:
GOMES, Heitor MGomes, H. M., Barddal, J. P., Boiko, L. E., & Bifet, A. (2018). Adaptive random forests for data stream regression. In Proc European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) (pp. 267-272).