Transparent Decision Support Using Statistical Evidence

dc.contributor.authorHamilton-Wright, Andrewen
dc.date.accessioned2006-08-22T13:57:39Z
dc.date.available2006-08-22T13:57:39Z
dc.date.issued2005en
dc.date.submitted2005en
dc.description.abstractAn automatically trained, statistically based, fuzzy inference system that functions as a classifier is produced. The hybrid system is designed specifically to be used as a decision support system. This hybrid system has several features which are of direct and immediate utility in the field of decision support, including a mechanism for the discovery of domain knowledge in the form of explanatory rules through the examination of training data; the evaluation of such rules using a simple probabilistic weighting mechanism; the incorporation of input uncertainty using the vagueness abstraction of fuzzy systems; and the provision of a strong confidence measure to predict the probability of system failure. <br /><br /> Analysis of the hybrid fuzzy system and its constituent parts allows commentary on the weighting scheme and performance of the "Pattern Discovery" system on which it is based. <br /><br /> Comparisons against other well known classifiers provide a benchmark of the performance of the hybrid system as well as insight into the relative strengths and weaknesses of the compared systems when functioning within continuous and mixed data domains. <br /><br /> Classifier reliability and confidence in each labelling are examined, using a selection of both synthetic data sets as well as some standard real-world examples. <br /><br /> An implementation of the work-flow of the system when used in a decision support context is presented, and the means by which the user interacts with the system is evaluated. <br /><br /> The final system performs, when measured as a classifier, comparably well or better than other classifiers. This provides a robust basis for making suggestions in the context of decision support. <br /><br /> The adaptation of the underlying statistical reasoning made by casting it into a fuzzy inference context provides a level of transparency which is difficult to match in decision support. The resulting linguistic support and decision exploration abilities make the system useful in a variety of decision support contexts. <br /><br /> Included in the analysis are case studies of heart and thyroid disease data, both drawn from the University of California, Irvine Machine Learning repository.en
dc.formatapplication/pdfen
dc.format.extent2656380 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10012/778
dc.language.isoenen
dc.pendingfalseen
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2005, Hamilton-Wright, Andrew. All rights reserved.en
dc.subjectSystems Designen
dc.subjectComputer Scienceen
dc.subjectElectrical & Computer Engineeringen
dc.subjectpattern recognitionen
dc.subjectdecision supporten
dc.subjectmachine learningen
dc.subjectfuzzy inferenceen
dc.subjecthuman-computer interactionen
dc.subjectprobabalistic systemsen
dc.subjectfuzzy systemsen
dc.subjectartificial intelligenceen
dc.titleTransparent Decision Support Using Statistical Evidenceen
dc.typeDoctoral Thesisen
uws-etd.degreeDoctor of Philosophyen
uws-etd.degree.departmentSystems Design Engineeringen
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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