Now showing items 1-3 of 3

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor M.; 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 ...
  • On dynamic feature weighting for feature drifting data streams

    Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabricio; 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, Fabricio; 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 ...