I am currently a senior research fellow at the University of Waikato in the machine learning group. My main research area is Machine Learning, specially Evolving Data Streams, Concept Drift, Ensemble methods and Big Data Streams.
I contribute to both MOA and StreamDM open data stream mining projects.
Our project is the recipient of the University of Waikato Strategic Research Fund! The project will be fully funded. Period January 2020 - December 2020.
The full list can be found in Publications.
Streaming Random Patches for Evolving Data Stream Classification (new!)
H M Gomes, J Read, A Bifet.
IEEE International Conference on Data Mining (ICDM), 2019.
The Streaming Random Patches (SRP) algorithm outperforms the current state-of-the-art ensemble methods for evolving data stream classification. Please contact me to have access to the preprint.
A Survey on Ensemble Learning for Data Stream Classification
H M Gomes, J P Barddal, F Enembreck, A Bifet.
ACM Computing Surveys 50, 2, Article 23, 2017.
Machine learning for streaming data: state of the art, challenges, and opportunities (new!)
H M Gomes, J Read, A Bifet, J P Barddal, J Gama
SIGKDD Explorations Newsletter, ACM , 2019.
In this work, we focus on elucidating the connections among the current stateof- the-art on related fields; and clarifying open challenges in both academia and industry. You can obtain it in the ACM library or the pre-print in my research gate.
Adaptive random forests for evolving data stream classiﬁcation
H M Gomes, A Bifet, J Read, ..., B Pfahringer, G Holmes, T Abdessalem
Machine Learning, Springer, 2017.
This paper presents an efficent version of the classical Random Forests algorithm for evolving data streams. You can obtain it in the Springer website, in here directly (Springer share), or the pre-print in my research gate.