Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-1995

Abstract

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network structure by removing excessive recognition categories and weights; and quantization of continuous learned weights, which allows the final system state to be translated into a usable set of descriptive rules. Three benchmark studies illustrate the rule extraction methods: (1) Pima Indian diabetes diagnosis, (2) mushroom classification and (3) DNA promoter recognition. Fuzzy ARTMAP and ART-EMAP are compared with the ADAP algorithm, the k nearest neighbor system, the back-propagation network and the C4.5 decision tree. The ARTMAP rule extraction procedure is also compared with the Knowledgetron and NOFM algorithms, which extract rules from back-propagation networks. Simulation results consistently indicate that ARTMAP rule extraction produces compact sets of comprehensible rules for which accuracy and complexity compare favorably to rules extracted by alternative algorithms.

Keywords

Fuzzy ARTMAP rule, confidence factor, pruning

Discipline

Databases and Information Systems | Systems Architecture | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Connection Science

Volume

7

Issue

1

First Page

3

Last Page

27

ISSN

0954-0091

Identifier

10.1080/09540099508915655

Publisher

Taylor and Francis

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1080/09540099508915655

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