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A general framework for designing a fuzzy rule-based classifier
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.ORCID-id: 0000-0003-2185-8973
Kaunas University of Technology.
Kaunas University of Technology.
Kaunas University of Technology.
2011 (engelsk)Inngår i: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 29, nr 1, s. 203-221Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.

sted, utgiver, år, opplag, sider
London: Springer London, 2011. Vol. 29, nr 1, s. 203-221
Emneord [en]
Classifier, Fuzzy rule, Genetic algorithm, Knowledge extraction, Variable selection, Evolving SOM tree
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-5825DOI: 10.1007/s10115-010-0340-xISI: 000295482900008Scopus ID: 2-s2.0-80053296433OAI: oai:DiVA.org:hh-5825DiVA, id: diva2:352177
Tilgjengelig fra: 2010-09-18 Laget: 2010-09-18 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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