Transparent decision support using statistical reasoning and fuzzy inference

• Published in: IEEE transactions on knowledge and data engineering 2006-08, Vol.18 (8), p.1125-1137
• Main Authors: Hamilton-Wright, A., Stashuk, D.W.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Confidence intervals; Construction; Data analysis; Data mining; decision support; Decision support systems; Exact sciences and technology; Fuzzy; fuzzy inference systems; Fuzzy logic; Fuzzy reasoning; Fuzzy rule-based classification systems; Fuzzy set theory; Fuzzy systems; human-centered computing; Inference; Information systems. Data bases; Knowledge bases (artificial intelligence); Learning and adaptive systems; Logic; Memory organisation. Data processing; Pattern analysis; pattern discovery; Probability; Software; software engineering; Studies; Training data; User interfaces
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2006.132

A framework for the development of a decision support system (DSS) that exhibits uncommonly transparent rule-based inference logic is introduced. A DSS is constructed by marrying a statistically based fuzzy inference system (FIS) with a user interface, allowing drill-down exploration of the underlying statistical support, providing transparent access to both the rule-based inference as well as the underlying statistical basis for the rules. The FIS is constructed through a "pattern discovery" based analysis of training data. Such an analysis yields a rule base characterized by simple explanations for any rule or data division in the extracted knowledge base. The reliability of a fuzzy inference is well predicted by a confidence measure that determines the probability of a correct suggestion by examination of values produced within the inference calculation. The combination of these components provides a means of constructing decision support systems that exhibit a degree of transparency beyond that commonly observed in supervised-learning-based methods. A prototype DSS is analyzed in terms of its workflow and usability, outlining the insight derived through use of the framework. This is demonstrated by considering a simple synthetic data example and a more interesting real-world example application with the goal of characterizing patients with respect to risk of heart disease. Specific input data samples and corresponding output suggestions created by the system are presented and discussed. The means by which the suggestions made by the system may be used in a larger decision context is evaluated