Confident interpretation of Bayesian decision tree ensembles for clinical applications.
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.
Schetinin
Vitaly
V
Computing and Information System Department, University of Bedfordshire, Luton LU1 3JU, UK. vitaly.schetinin@beds.ac.uk
Fieldsend
Jonathan E
JE
Partridge
Derek
D
Coats
Timothy J
TJ
Krzanowski
Wojtek J
WJ
Everson
Richard M
RM
Bailey
Trevor C
TC
Hernandez
Adolfo
A
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D009010:Monte Carlo Method; D010363:Pattern Recognition, Automated / Q000379:methods*