Now showing items 1-5 of 10

  • 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 ...
  • Evaluation methods and decision theory for classification of streaming data with temporal dependence

    Žliobaitė, Indrė; Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2014)
    Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over ...