Dr Heitor Murilo Gomes
Senior 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.
Recent Publications
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., 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
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
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: https://hdl.handle.net/10289/12538