Now showing items 1-6 of 6

  • The Bayesian Decision Tree Technique with a Sweeping Strategy 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (2004)
    The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably ...
  • A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Bailey, Trevor C.; Everson, Richard M.; Hernandez, Adolfo (IOS Press, 2006)
  • Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (Springer, 2006)
    Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off ...
  • Confident interpretation of Bayesian decision tree ensembles for clinical applications 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Coats, Timothy J.; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (Institute of Electrical and Electronics Engineers (IEEE), 2007)
    Bayesian averaging (BA) over ensembles of decision models allows evaluation of the uncertainty of decisions that is of crucial importance for safety-critical applications such as medical diagnostics. The interpretability ...
  • Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (Springer Berlin Heidelberg, 2007)
    Summary Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in ...
  • Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles 

    Schetinin, Vitaly; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Fieldsend, Jonathan E.; Bailey, Trevor C.; Hernandez, Adolfo (Springer Berlin Heidelberg, 2004)
    In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on ...