Browsing by Author "Gomes, Heitor Murilo"

Now showing items 6-9 of 9

  • On dynamic feature weighting for feature drifting data streams

    Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabrício; Pfahringer, Bernhard; Bifet, Albert (Springer, 2016)
    The ubiquity of data streams has been encouraging the development of new incremental and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but mainly, tailored to adapt to possible changes ...
  • On ensemble techniques for data stream regression

    Gomes, Heitor Murilo; Montiel, Jacob; Mastelini, Saulo Martiello; Pfahringer, Bernhard; Bifet, Albert (IEEE, 2020)
    An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were ...
  • Performance measures for evolving predictions under delayed labelling classification

    Grzenda, Maciej; Gomes, Heitor Murilo; Bifet, Albert (IEEE, 2020)
    For many streaming classification tasks, the ground truth labels become available with a non-negligible latency. Given this delayed labelling setting, after the instance data arrives and before its true label is known, the ...
  • A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

    Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabrício; Pfahringer, Bernhard (Elsevier, 2017)
    Data stream mining is a fast growing research topic due to the ubiquity of data in several real-world problems. Given their ephemeral nature, data stream sources are expected to undergo changes in data distribution, a ...