16 citations · 45 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Neural-Network Techniques for Visual Mining Clinical Electroencephalograms
Vitaly Schetinin, Joachim Schult, Anatoly Brazhnikov
In this chapter we describe new neural-network techniques developed for visual mining clinical electroencephalograms (EEGs), the weak electrical potentials invoked by brain activit…
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…
Learning Polynomial Networks for Classification of Clinical Electroencephalograms
Vitaly Schetinin, Joachim Schult
We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy impl…