Now showing items 1-4 of 4

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfahringer, Bernhard; Holmes, Geoffrey; Abdessalem, Talel (Springer, 2017)
    Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ...
  • Boosting decision stumps for dynamic feature selection on data streams

    Barddal, Jean Paul; Enembreck, Fabrício; Gomes, Heitor Murilo; Bifet, Albert; Pfahringer, Bernhard (2019)
    Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease ...
  • 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 ...
  • 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 ...