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Self-Confidence Measures of a Decision Support System Based on Bayesian Networks

Kozniewski, Marcin (2019) Self-Confidence Measures of a Decision Support System Based on Bayesian Networks. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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A prominent formalism used in decision support is decision theory, which relies on probability theory to model uncertainty about unknown information. A decision support system relying on this theory produces conditional probability as a response. The quality of a decision support system's response depends on three key factors: the amount of data available to train the model, the amount of information about the case at hand, and the adequacy of the system's model to the case at hand.

In this dissertation, I investigate different approaches to measuring the confidence of decision support systems based on Bayesian networks addressing the three key factors mentioned above. Some of such confidence measures of the system response have been already proposed. I propose and discuss other measures based on analysis of joint probability distribution encoded by a Bayesian network.

The main contribution of this dissertation is the analysis of the discussed measures whether they provide useful information about the performance of a Bayesian network model. I start the analysis with an investigation of interactions among these measures. Then, I investigate whether confidence measures help us predict an erroneous response of a classifier based on Bayesian networks when applied to a particular case. The results suggest that the discussed measures may be considered as indicators for possible mistakes in classification. Further, I conduct an experiment to check how confidence measures perform in combining the models' output in the ensemble of classifiers by weighting.

Based on the findings presented in this dissertation, I conclude that the confidence measures may enrich the decision support system's output to serve as indicators for applicability of the model and its advice to a given case.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kozniewski, Marcinmak295@pitt.edumak2950000-0002-9552-0679
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorDruzdzel, Marek J.druzdzel@pitt.eduDRUZDZEL0000-0002-7598-2286
Committee MemberHirtle, Stephen C.hirtle@pitt.eduHIRTLE0000-0001-9621-2769
Committee MemberMunro, Paul W.pwm@pitt.eduPWM0000-0003-2398-9248
Committee MemberAntaki, James F.antaki@cornell.edu0000-0002-5430-7353
Date: 22 May 2019
Date Type: Publication
Defense Date: 11 April 2019
Approval Date: 22 May 2019
Submission Date: 22 April 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 112
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Bayesian networks, decision support systems, probabilistic graphical models, confidence measures
Date Deposited: 22 May 2019 12:33
Last Modified: 22 May 2019 12:33

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