Now showing items 1-5 of 9

  • Bagging ensemble selection

    Sun, Quan; Pfahringer, Bernhard (Springer, 2011)
    Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The ...
  • 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 ...
  • Evolving artificial datasets to improve interpretable classifiers

    Mayo, Michael; Sun, Quan (IEEE, 2014)
    Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple ...
  • Full model selection in the space of data mining operators

    Sun, Quan; Pfahringer, Bernhard; Mayo, Michael (ACM, 2012)
    We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for ...
  • Hierarchical meta-rules for scalable meta-learning

    Sun, Quan; Pfahringer, Bernhard (Springer Verlag, 2014)
    The Pairwise Meta-Rules (PMR) method proposed in [18] has been shown to improve the predictive performances of several metalearning algorithms for the algorithm ranking problem. Given m target objects (e.g., algorithms), ...

Quan Sun has 2 co-authors in Research Commons.