Browsing by Author "Mitchell, Rory"

Now showing items 1-5 of 6

  • Accelerating the XGBoost algorithm using GPU computing

    Mitchell, Rory; Frank, Eibe (2017)
    We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and ...
  • 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 ...
  • An empirical study of moment estimators for quantile approximation

    Mitchell, Rory; Frank, Eibe; Holmes, Geoffrey (Association for Computing Machinery (ACM), 2021)
    We empirically evaluate lightweight moment estimators for the single-pass quantile approximation problem, including maximum entropy methods and orthogonal series with Fourier, Cosine, Legendre, Chebyshev and Hermite basis ...
  • GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles

    Mitchell, Rory; Frank, Eibe; Holmes, Geoffrey (PeerJ, 2022)
    SHapley Additive exPlanation (SHAP) values (Lundberg & Lee, 2017) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values (Shapley, 1953). While exact calculation of ...
  • High-throughput machine learning algorithms

    Mitchell, Rory (The University of Waikato, 2021)
    The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. ...