16 citations · 16 across the 4 of their papers we have counts for
4 papers
Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique
Vitaly Schetinin, Jonathan E. Fieldsend, Derek Partridge +4
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of…
Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data
Vitaly Schetinin, Jonathan E. Fieldsend, Derek Partridge +4
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in app…
Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles
V. Schetinin, D. Partridge, W. J. Krzanowski +4
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 strat…
The Bayesian Decision Tree Technique with a Sweeping Strategy
V. Schetinin, J. E. Fieldsend, D. Partridge +4
The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the unce…