Browsing by Author "Bifet, Albert"

Now showing items 6-10 of 46

  • 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 ...
  • Change detection in categorical evolving data streams

    Ienco, Dino; Bifet, Albert; Pfahringer, Bernhard; Poncelet, Pascal (ACM, 2014)
    Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical ...
  • 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 ...
  • Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within MOA

    Kranen, Philipp; Kremer, Hardy; Jensen, Timm; Seidl, Thomas; Bifet, Albert; Homes, Geoff; Pfahringer, Bernhard (IEEE Computer Society, 2010)
    In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good ...
  • confstream: automated algorithm selection and configuration of stream clustering algorithms

    Carnein, Matthias; Trautmann, Heike; Bifet, Albert; Pfahringer, Bernhard (Springer, 2020)
    Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult ...