Now showing items 1-5 of 11

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor M.; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfharinger, 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 ...
  • Batch-incremental versus instance-incremental learning in dynamic and evolving data

    Read, Jesse; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2012)
    Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically- infinite stream of examples using limited time and memory, while being ...
  • Classifier chains for multi-label classification

    Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe (Springer, 2009)
    The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence ...
  • Efficient data stream classification via probabilistic adaptive windows

    Bifet, Albert; Pfahringer, Bernhard; Read, Jesse; Holmes, Geoffrey (ACM, 2013)
    In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this ...
  • Efficient multi-label classification for evolving data streams

    Read, Jesse; Bifet, Albert; Holmes, Geoffrey; Pfahringer, Bernhard (University of Waikato, Department of Computer Science, 2010-05)
    Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios ...