Rule Extraction, Fuzzy ARTMAP, and Medical DatabasesReport as inadecuate


Rule Extraction, Fuzzy ARTMAP, and Medical Databases


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Abstract

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes PID database, illustrate the method. In the simulations, pruned networks about 1-3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.Rights

Copyright 1993 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and - or special permission.

CAS-CNS Technical Reports -



Author: Carpenter, Gail A. - Tan, Ah-Hwee - -

Source: https://open.bu.edu/



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