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dc.contributor.author Chen, Tianhua
dc.contributor.author Shen, Qiang
dc.contributor.author Su, Pan
dc.contributor.author Shang, Changjing
dc.date.accessioned 2016-03-31T22:01:10Z
dc.date.available 2016-03-31T22:01:10Z
dc.date.issued 2015-11-12
dc.identifier.citation Chen , T , Shen , Q , Su , P & Shang , C 2015 , ' Fuzzy rule weight modification with particle swarm optimisation ' Soft Computing , pp. 1-15 . , 10.1007/s00500-015-1922-z en
dc.identifier.issn 1432-7643
dc.identifier.other PURE: 6741623
dc.identifier.other PURE UUID: 0e518f30-c2bc-4b3b-8362-49401159c014
dc.identifier.uri http://hdl.handle.net/2160/42531
dc.description This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s00500-015-1922-z en
dc.description.abstract The most challenging problem in developing fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. In many practical applications, fuzzy sets that are of particular linguistic meanings, are often predefined by domain experts and required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any qualification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weights of fuzzy rules can help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, entailing high classification accuracy. Systematic experimental studies are carried out using common benchmark data sets, in comparison to popular rule based learning classifiers. The results demonstrate that the proposed approach can boost classification performance, especially when the size of the initially built rule base is relatively small, and is competitive to popular rule-based learning classifiers. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof Soft Computing en
dc.subject fuzzy rule induction en
dc.subject fuzzy rule weights en
dc.subject rule weight modification en
dc.subject particle swarm optimisation en
dc.title Fuzzy rule weight modification with particle swarm optimisation en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.identifier.doi http://dx.doi.org/10.1007/s00500-015-1922-z
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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