Dr Heitor Murilo Gomes

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Senior Research Fellow (Computer Science)

Qualifications: BSc UTP MSc PhD PUCPR

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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

  • Barddal, J. P., Gomes, H. M., Enembreck, F., & Pfahringer, B. (2017). A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. Journal of Systems and Software, 127, 278-294. doi:10.1016/j.jss.2016.07.005 Open Access version:

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