Exploiting decision trees in product-based fuzzy neural modeling to generate rules with dynamically reduced dimensionality

Pertselakis, M; Stafylopatis, A

dc.contributor.author	Pertselakis, M	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:24:24Z
dc.date.available	2014-03-01T01:24:24Z
dc.date.issued	2006	en
dc.identifier.issn	15715736	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17241
dc.subject	Feature Selection	en
dc.subject	Fuzzy Inference System	en
dc.subject	Fuzzy Rules	en
dc.subject	Neural Model	en
dc.subject	Real World Application	en
dc.subject	Rule Extraction	en
dc.subject	Decision Tree	en
dc.title	Exploiting decision trees in product-based fuzzy neural modeling to generate rules with dynamically reduced dimensionality	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/0-387-34224-9_3	en
heal.identifier.secondary	http://dx.doi.org/10.1007/0-387-34224-9_3	en
heal.publicationDate	2006	en
heal.abstract	Decision trees are commonly employed as data classifiers in various research fields, but also in real-world application domains. In the fuzzy neural framework, decision trees can offer valuable assistance in determining a proper initial system structure, which means not only feature selection, but also rule extraction and organization. This paper proposes a synergistic model that combines the advantages of a subsethood-product neural fuzzy inference system and a CART algorithm, in order to create a novel architecture and generate fuzzy rules of the form ""IF - THEN IF"", where the first ""IF"" concerns the primary attributes and the second ""IF"" the secondary attributes of the given dataset as defined by our method. The resulted structure eliminates certain drawbacks of both techniques and produces a compact, comprehensible and efficient rulebase. Experiments in benchmark classification tasks prove that this method does not only reduce computational cost, but it also maintains performance at high levels, offering fast and accurate processing during realtime operations. © 2006 International Federation for Information Processing.	en
heal.journalName	IFIP International Federation for Information Processing	en
dc.identifier.doi	10.1007/0-387-34224-9_3	en
dc.identifier.volume	204	en
dc.identifier.spage	19	en
dc.identifier.epage	26	en