Dynamic modular fuzzy neural classifier with tree-based structure identification

Pertselakis, M; Stafylopatis, A

dc.contributor.author	Pertselakis, M	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:28:11Z
dc.date.available	2014-03-01T01:28:11Z
dc.date.issued	2008	en
dc.identifier.issn	0925-2312	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18749
dc.subject	Decision trees	en
dc.subject	Modular multi-net systems	en
dc.subject	Neuro-fuzzy modeling	en
dc.subject	Pattern recognition	en
dc.subject	Structure identification	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Computer simulation	en
dc.subject.other	Decision trees	en
dc.subject.other	Expert systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Set theory	en
dc.subject.other	Supervised learning	en
dc.subject.other	Classification and regression trees algorithms	en
dc.subject.other	Fuzzy neural classifier	en
dc.subject.other	Modular multi-net systems	en
dc.subject.other	Neuro-fuzzy modeling	en
dc.subject.other	Structure identification	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	accuracy	en
dc.subject.other	article	en
dc.subject.other	automated pattern recognition	en
dc.subject.other	classification algorithm	en
dc.subject.other	computer analysis	en
dc.subject.other	computer interface	en
dc.subject.other	computer language	en
dc.subject.other	dynamics	en
dc.subject.other	fuzzy system	en
dc.subject.other	information processing	en
dc.subject.other	machine learning	en
dc.subject.other	mathematical analysis	en
dc.subject.other	mathematical computing	en
dc.subject.other	priority journal	en
dc.subject.other	process optimization	en
dc.title	Dynamic modular fuzzy neural classifier with tree-based structure identification	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.neucom.2007.03.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.neucom.2007.03.009	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Inspired by a modular way of reasoning, we present a subsethood-product fuzzy neural classifier with a novel, robust and dynamic architecture, involving a main module and a number of submodules. The well-known classification and regression trees (CART) algorithm is employed as a fast preprocess of structure identification, which divides the input space into high certainty and low certainty regions, each representing a primary fuzzy rule. These primary fuzzy rules use a minimum set of attributes and are mapped onto the main neuro-fuzzy module. However, the patterns belonging to a low certainty primary rule get further split into a subset of secondary rules that use an extended set of attributes. Each such rule subset is mapped onto an expert-submodule, which gets activated only when a pattern falls into the respective low certainty region. In other words, we create a rule form of ""if-then-if"" conditional statement, where the first ""IF"" concerns the main module and the primary rule, while the second ""IF"" concerns the respective submodule and the secondary rule set. This dynamic resource-allocating model is optimized through a supervised learning procedure. Experiments in benchmark classification tasks prove that this architecture not only does reduce complexity and computational cost, which is its primary goal, but also offers fast and accurate processing during real-time operation. Moreover, it holds certain properties that make it ideal for soft computing applications of high dimension, especially those that adopt user-profiles or require partial re-training. © 2007 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Neurocomputing	en
dc.identifier.doi	10.1016/j.neucom.2007.03.009	en
dc.identifier.isi	ISI:000253663800038	en
dc.identifier.volume	71	en
dc.identifier.issue	4-6	en
dc.identifier.spage	801	en
dc.identifier.epage	812	en