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|Title: ||Transparent Decision Support Using Statistical Evidence|
|Authors: ||Hamilton-Wright, Andrew|
|Keywords: ||Systems Design|
Electrical & Computer Engineering
|Approved Date: ||2005 |
|Date Submitted: ||2005 |
|Abstract: ||An 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. |
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.
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.
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.
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.
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.
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.
Included in the analysis are case studies of heart and thyroid disease data, both drawn from the University of California, Irvine Machine Learning repository.
|Department: ||Systems Design Engineering|
|Degree: ||Doctor of Philosophy|
|Appears in Collections:||Faculty of Engineering Theses and Dissertations |
Electronic Theses and Dissertations (UW)
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