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Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods

✍ Scribed by Oscar Cordón; María José del Jesus; Francisco Herrera

Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
247 KB
Volume
13
Category
Article
ISSN
0884-8173

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✦ Synopsis

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms. The results show the good behavior of the proposed method, which maintains the linguistic description of the fuzzy rules.

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MOGUL: A methodology to obtain genetic f
MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach
✍ O. Cordón; M. J. del Jesus; F. Herrera; M. Lozano 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 219 KB

main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based process