Now showing items 1-5 of 9

  • Algorithm selection on data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (Springer International Publishing, 2014)
    We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier ...
  • Case study on bagging stable classifiers for data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (2015)
    Ensembles of classifiers are among the strongest classi-fiers in most data mining applications. Bagging ensembles exploit the instability of base-classifiers by training them on different bootstrap replicates. It has been ...
  • Experiment databases: A new way to share, organize and learn from experiments

    Vanschoren, Joaquin; Blockeel, Hendrik; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2012)
    Thousands of machine learning research papers contain extensive experimental comparisons. However, the details of those experiments are often lost after publication, making it impossible to reuse these experiments in further ...
  • Experiment Databases: Creating a New Platform for Meta-Learning Research

    Vanschoren, Joaquin; Blockeel, Hendrik; Pfahringer, Bernhard; Holmes, Geoffrey (University of Porto, 2008)
    Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning ...
  • Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (IEEE, 2015-01-01)
    Ensembles of classifiers are among the best performing classifiers available in many data mining applications. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of ...

Joaquin Vanschoren has 5 co-authors in Research Commons.