Now showing items 6-10 of 111

  • Bagging ensemble selection for regression

    Sun, Quan; Pfahringer, Bernhard (Springer, 2012)
    Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on ...
  • 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 ...
  • 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 ...
  • Bound analysis for Whiley programs

    Weng, Min-Hsien; Utting, Mark; Pfahringer, Bernhard (Elsevier, 2015)
    The Whiley compiler can generate naive C code, but the code is inefficient because it uses infinite integers and dynamic array sizes. Our project goal is to build up a compiler that can translate Whiley programs into ...
  • Building a Twitter opinion lexicon from automatically-annotated tweets

    Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard (Elsevier, 2016-09-15)
    Opinion lexicons, which are lists of terms labelled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations ...