Dr Heitor Murilo Gomes

Postdoctoral Fellow (Computer Science)

Qualifications: BSc UTP MSc PhD PUCPR

Contact Details

Room: FG.2.03
Phone: +64 7 838 4104

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). Merit-guided dynamic feature selection filter for data streams. Expert Systems with Applications, 116, 227-242. doi:10.1016/j.eswa.2018.09.031

  • 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

  • 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., Barddal, J. P., Enembreck, F., & Bifet, A. (2017). A survey on ensemble learning for data stream classification. ACM Computing Surveys, 50(2), 1-36. doi:10.1145/3054925

Find more research publications by Heitor Murilo Gomes