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research
Confident interpretation of Bayesian decision tree ensembles for clinical applications
Authors
Trevor C. Bailey
Timothy J. Coats
+6 more
Richard M. Everson
Jonathan E. Fieldsend
Adolfo Hernandez
Wojtek J. Krzanowski
Derek Partridge
Vitaly Schetinin
Publication date
27 June 2013
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
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Abstract
Copyright © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.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 of the ensemble can also give useful information for experts responsible for making reliable decisions. For this reason, decision trees (DTs) are attractive decision models for experts. However, BA over such models makes an ensemble of DTs uninterpretable. In this paper, we present a new approach to probabilistic interpretation of Bayesian DT ensembles. This approach is based on the quantitative evaluation of uncertainty of the DTs, and allows experts to find a DT that provides a high predictive accuracy and confident outcomes. To make the BA over DTs feasible in our experiments, we use a Markov Chain Monte Carlo technique with a reversible jump extension. The results obtained from clinical data show that in terms of predictive accuracy, the proposed method outperforms the maximum a posteriori (MAP) method that has been suggested for interpretation of DT ensembles
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Last time updated on 06/08/2013
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