Browsing by Author "Abdessalem, Talel"

Now showing items 1-3 of 3

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; 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 ...
  • Adaptive XGBoost for evolving data streams

    Montiel, Jacob; Mitchell, Rory; Frank, Eibe; Pfahringer, Bernhard; Abdessalem, Talel; Bifet, Albert (IEEE, 2020)
    Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present ...
  • River: Machine learning for streaming data in Python

    Montiel, Jacob; Halford, Max; Mastelini, Saulo Martiello; Bolmier, Geoffrey; Sourty, Raphael; Vaysse, Robin; Zouitine, Adil; Gomes, Heitor Murilo; Read, Jesse; Abdessalem, Talel; Bifet, Albert (2021)
    River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different ...