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Symbolic classification, clustering and fuzzy radial basis function network. (English) Zbl 1101.68775
Summary: Symbolic fuzzy classification is proposed using fuzzy radial basis function network, with fuzzy $c$-medoids clustering at the hidden layer. Symbolic objects include linguistic, nominal, boolean and interval-type of features, along with quantitative attributes. Classification and clustering in this domain involve the use of symbolic dissimilarity between the objects. Fuzzy memberships are used for appropriately handling uncertainty inherent in real-life decisions. The fuzzy radial basis function network here comprises an integration of the principles of radial basis function network and fuzzy c-medoids clustering, for handling non-numeric data. The optimal number of hidden nodes is determined by using clustering validity indices, like normalized modified Hubert’s statistic and Davies-Bouldin index, in the symbolic framework. The effectiveness of the symbolic fuzzy classification is demonstrated on real-life benchmark data sets. Comparison is provided with the performance of a decision tree.

##### MSC:
 68T05 Learning and adaptive systems 68T10 Pattern recognition, speech recognition
UCI-ml
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##### References:
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