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Now showing items 1-10 of 10

  • 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 ...
  • Bayesian inductively learned modules for safety critical systems 

    Fieldsend, Jonathan E.; Bailey, Trevor C.; Everson, Richard M.; Krzanowski, Wojtek J.; Partridge, Derek; Schetinin, Vitaly (Interface Foundation of North America, Inc., 2003)
    This work examines the use of Bayesian inductively learned software modules for safety critical systems. Central to the safety critical application is the desire to generate confidence measures associated with predictions. ...
  • 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 ...
  • Computing with confidence: a Bayesian approach 

    Partridge, Derek; Fieldsend, Jonathan E.; Krzanowski, Wojtek J.; Bailey, Trevor C.; Everson, Richard M.; Schetinin, Vitaly (2006)
    Bayes’ rule is introduced as a coherent strategy for multiple recomputations of classifier system output, and thus as a basis for assessing the uncertainty associated with a particular system results --- i.e. a basis for ...
  • Confidence in Classification: A Bayesian Approach 

    Krzanowski, Wojtek J.; Bailey, Trevor C.; Partridge, Derek; Fieldsend, Jonathan E.; Everson, Richard M.; Schetinin, Vitaly (Springer Verlag, 2006)
    Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic ...
  • 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 ...
  • Representing classifier confidence in the safety critical domain: an illustration from mortality prediction in trauma cases 

    Bailey, Trevor C.; Everson, Richard M.; Fieldsend, Jonathan E.; Krzanowski, Wojtek J.; Partridge, Derek; Schetinin, Vitaly (Springer Verlag, 2007)
    This work proposes a novel approach to assessing confidence measures for software classification systems in demanding applications such as those in the safety critical domain. Our focus is the Bayesian framework for ...