Browsing by Author "Montiel, Jacob"

Now showing items 1-3 of 3

  • 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 ...
  • On ensemble techniques for data stream regression

    Gomes, Heitor Murilo; Montiel, Jacob; Mastelini, Saulo Martiello; Pfahringer, Bernhard; Bifet, Albert (IEEE, 2020)
    An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were ...
  • 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 ...