Marginal Maximum Entropy Partitioning Yields Asymptotically Consistent Probability Density Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Association to Classification: Inference Using Weight of Evidence
IEEE Transactions on Knowledge and Data Engineering
Pattern discovery: a data driven approach to decision support
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy Classification Using Pattern Discovery
IEEE Transactions on Fuzzy Systems
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A framework for a decision support system (DSS) is introduced. This framework supports the exploration of quantitative electromyographic (QEMG) data acquired using a concentric needle electrode and decomposed using the DQEMG program. This DSS has been constructed by marrying a statistically based fuzzy inference system (FIS) with a user interface, allowing drill-down exploration of the underlying statistical support, providing an exceptionally 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 during the inference calculation. The combination of these components provides a means of supporting the characterization of neuromuscular disorders in a clinical environment, providing the transparent support required to allow a clinical user to confidently incorporate the automatic suggestions provided by this framework into a larger decision context. Specific data examples based on the characterization of QEMG data are used to demonstrate the principles of the system.