Dr Heitor Murilo Gomes
Research Fellow (Computer Science)
Qualifications: BSc UTP MSc PhD PUCPR
Contact Details
Email: hgomes@waikato.ac.nz
Room: FG.2.03
Phone: +64 7 838 4104
Website: https://www.heitorgomes.com
About Heitor Murilo
Since his undergraduate studies, Heitor focuses his research in machine learning and data mining. This has not changed since, but now he focuses mainly on machine learning for data streams.
Research Interests
Data stream mining, ensemble methods, semi-supervised learning, feature selection.
Research Supervised
Recent Publications
Gomes, 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. doi:10.1007/s10994-019-05793-3
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
Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., . . . Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, 106(9-10), 1469-1495. doi:10.1007/s10994-017-5642-8 Open Access version: https://hdl.handle.net/10289/11231