GA-Based Construction of Fuzzy Classifiers Using Information Granules

Title
GA-Based Construction of Fuzzy Classifiers Using Information Granules
Authors
이호재
Keywords
fuzzy classifier fuzzy set information granules genetic algorithm
Issue Date
2006
Publisher
INST CONTROL AUTOMATION & SYSTEMS ENGINEERS-KOREAN INST ELECTRICAL ENG
Series/Report no.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS; Vol.4 No.2
Abstract
A new GA-based methodology using information granules is suggested for the construction of Fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA is utilized for tuning of the fuzzy rules, which can enhance the classification performance oil the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, all example, the classification of the Iris data, is provided.
URI
http://dspace.inha.ac.kr/handle/10505/29432
ISSN
1598-6446
Appears in Collections:
College of Engineering(공과대학) > Electrical Engineering (전기공학) > Local Access Journal, Report (전기공학 논문, 보고서)

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