Now showing items 1-3 of 3

  • Clustering based active learning for evolving data streams

    Ienco, Dino; Bifet, Albert; Žliobaitė, Indrė; Pfahringer, Bernhard (Springer, 2013)
    Data labeling is an expensive and time-consuming task. Choosing which labels to use is increasingly becoming important. In the active learning setting, a classifier is trained by asking for labels for only a small fraction ...
  • Evaluation methods and decision theory for classification of streaming data with temporal dependence

    Žliobaitė, Indrė; Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2015)
    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 ...
  • Pitfalls in benchmarking data stream classification and how to avoid them

    Bifet, Albert; Read, Jesse; Žliobaitė, Indrė; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2013)
    Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data ...

Indrė Žliobaitė has 5 co-authors in Research Commons.