Heitor Murilo Gomes

Heitor Murilo Gomes

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 MOA (Java), StreamDM (Spark Streaming) and scikit-multiflow (Python) open data stream mining projects.

Heitor Murilo Gomes

I am a machine learning researcher and developer working at the University of Waikato (New Zealand).

Selected Publications

Streaming Random Patches for Evolving Data Stream Classification

H M Gomes, J Read, A Bifet. IEEE International Conference on Data Mining (ICDM), 2019. DOI: https://doi.org/10.1109/ICDM.2019.00034

The Streaming Random Patches (SRP) algorithm outperforms the current state-of-the-art ensemble methods for evolving data stream classification. Access Paper

Machine learning for streaming data: state of the art, challenges, and opportunities

H M Gomes, J Read, A Bifet, J P Barddal, J Gama. SIGKDD Explorations Newsletter, ACM , 2019. DOI: https://doi.org/10.1145/3373464.3373470

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. Access Paper

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. DOI: https://doi.org/10.1145/3054925

This paper contains the most up to date and comprehensive survey about ensemble learning for data streams. Access Paper

Adaptive random forests for evolving data stream classification

H M Gomes, A Bifet, J Read, …, B Pfahringer, G Holmes, T Abdessalem. ACM Machine Learning, Springer, 2017. DOI: https://doi.org/10.1007/s10994-017-5642-8

This paper presents an efficent version of the classical Random Forests algorithm for evolving data streams, namely the Adaptive Random Forest (ARF) algorithm. Access Paper

News 2020

  • (September 2020) New website
    • Going through the process of migrating stuff from the old website to the new one.
    • I am co-chair of the DS track on ACM SAC 2021. Read more
    • Attending ECML 2020.
  • (August 2020) The MOA Lab
    • The MOA Lab is open (University of Waikato, FG Link build).
    • One week vacations in the South Island :)
  • (July 2020) scikit-multiflow and papers
    • Paper accepted on Discovery Science 2020.
    • Presented the tutorial entitled ‘Machine learning for data streams in Python with scikit-multi flow’ at IJCNN 2020 with Jacob Montiel, Jesse Read and Albert Bifet.
    • New MOA 20.07 release. Read more.
    • Blog entry about SRP on the MOA website. Read more.
  • (June 2020) COMPX523 wrap-up and papers!
    • Paper accepted on DAWAK 2020.
    • COMPX523 (Data stream mining) 2020 has ended. It was interesting to teach over Zoom, but I prefer to teach in a classroom.
  • (May 2020) Chaired a Session for PAKDD 2020
  • (April 2020) Honour Roll of Outstanding Reviewers for PAKDD 2020
  • (March 2020) COMPX523 and paper
    • Paper accepted on IJCAI 2020
    • COMPX523 Data stream mining paper started at UoW. It was quickly moved to online teaching due to the lockdown.
    • Tutorial 6: Building MOA from the source. Read more.

  • (February 2020) Papers accepted on IJCNN 2020

  • (January 2020) Distributed ML DS Project kick-off