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Transparent Decision Support Using Statistical Reasoning and Fuzzy Inference
August 2006 (vol. 18 no. 8)
pp. 1125-1137
ASCII Text
x 
 
Andrew Hamilton-Wright, Daniel W. Stashuk, "Transparent Decision Support Using Statistical Reasoning and Fuzzy Inference," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1125-1137, August, 2006.
 
 
BibTex
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@article{ 10.1109/TKDE.2006.132,
author = {Andrew Hamilton-Wright and Daniel W. Stashuk},
title = {Transparent Decision Support Using Statistical Reasoning and Fuzzy Inference},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {18},
number = {8},
issn = {1041-4347},
year = {2006},
pages = {1125-1137},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.132},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Transparent Decision Support Using Statistical Reasoning and Fuzzy Inference
IS - 8
SN - 1041-4347
SP1125
EP1137
EPD - 1125-1137
A1 - Andrew Hamilton-Wright,
A1 - Daniel W. Stashuk,
PY - 2006
KW - Fuzzy rule-based classification systems
KW - fuzzy inference systems
KW - pattern discovery
KW - decision support
KW - software engineering
KW - human-centered computing.
VL - 18
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
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.

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Index Terms:
Fuzzy rule-based classification systems, fuzzy inference systems, pattern discovery, decision support, software engineering, human-centered computing.
Citation:
Andrew Hamilton-Wright, Daniel W. Stashuk, "Transparent Decision Support Using Statistical Reasoning and Fuzzy Inference," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1125-1137, Aug. 2006, doi:10.1109/TKDE.2006.132
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