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Identification of a Gaussian Fuzzy Classifier  

Heesoo Hwang (School of Electrical Engineering, Halla University)
Publication Information
International Journal of Control, Automation, and Systems / v.2, no.1, 2004 , pp. 118-124 More about this Journal
Abstract
This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.
Keywords
Fuzzy classifier; pattern classification; clustering; differential evolution.;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 3
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