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
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
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
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11953 LNCS (pp. 704-716). doi:10.1007/978-3-030-36708-4_58
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