Fuzzy neural network approach to classification based on proximity characteristics of patterns

Blekas, K; Likas, A; Stafylopatis, A

dc.contributor.author	Blekas, K	en
dc.contributor.author	Likas, A	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:41:17Z
dc.date.available	2014-03-01T02:41:17Z
dc.date.issued	1997	en
dc.identifier.issn	10636730	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30447
dc.subject	Fuzzy Clustering	en
dc.subject	Fuzzy Neural Network	en
dc.subject	Fuzzy Set	en
dc.subject	Neural Network Classifier	en
dc.subject	Grade of Membership	en
dc.subject	voronoi diagram	en
dc.subject.other	Algorithms	en
dc.subject.other	Character recognition	en
dc.subject.other	Decision theory	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Probability	en
dc.subject.other	Voronoi diagram	en
dc.subject.other	Neural networks	en
dc.title	Fuzzy neural network approach to classification based on proximity characteristics of patterns	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/TAI.1997.632272	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TAI.1997.632272	en
heal.publicationDate	1997	en
heal.abstract	A neural network classifier is presented, which is based on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns, an aggregation of Voronoi regions is performed leading to the identification of larger regions belonging exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations concerning the grade of membership of the training patterns to the created regions, decision probabilities are computed through which the final classification is performed. Experimental results concerning several classification problems indicate that the proposed method achieves high classification rates and compares favorably with other well-known approaches.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Proceedings of the International Conference on Tools with Artificial Intelligence	en
dc.identifier.doi	10.1109/TAI.1997.632272	en
dc.identifier.spage	323	en
dc.identifier.epage	330	en