Breadcrumbs

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

Find more research publications by Heitor Murilo Gomes