Transparent Decision Support Using Statistical Evidence
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.
School:University of Waterloo
School Location:Canada - Ontario
Source Type:Master's Thesis
Keywords:systems design pattern recognition decision support machine learning fuzzy inference human computer interaction probabalistic artificial intelligence
Date of Publication:01/01/2005