Application of the fuzzy min-max neural network classifier to problems with continuous and discrete attributes

Likas, A; Blekas, K; Stafylopatis, A

dc.contributor.author	Likas, A	en
dc.contributor.author	Blekas, K	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:41:00Z
dc.date.available	2014-03-01T02:41:00Z
dc.date.issued	1994	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30317
dc.subject	Fuzzy Set	en
dc.subject	Neural Network Classifier	en
dc.subject	Pattern Recognition	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Computational complexity	en
dc.subject.other	Computational methods	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Learning systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Discrete dimensions	en
dc.subject.other	Fuzzy min max classification network	en
dc.subject.other	Hyperbox fuzzy sets	en
dc.subject.other	Network training	en
dc.subject.other	Neural networks	en
dc.title	Application of the fuzzy min-max neural network classifier to problems with continuous and discrete attributes	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/NNSP.1994.366052	en
heal.identifier.secondary	http://dx.doi.org/10.1109/NNSP.1994.366052	en
heal.publicationDate	1994	en
heal.abstract	The fuzzy min-max classification network constitutes a promising pattern recognition approach that is based on hyberbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The definition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max network to a problem with continuous and discrete attributes, requires the modification of its definition and operation in order to deal with the discrete dimensions. Experimental results using the modified model on a difficult pattern recognition problem establishes the strengths and weaknesses of the proposed approach.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop	en
dc.identifier.doi	10.1109/NNSP.1994.366052	en
dc.identifier.spage	163	en
dc.identifier.epage	170	en