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dc.contributor.author Shen, Qiang
dc.contributor.author Ellis, Ian O.
dc.contributor.author Garibaldi, Jonathan M.
dc.contributor.author Rasmani, Khairul A.
dc.date.accessioned 2009-09-04T08:55:05Z
dc.date.available 2009-09-04T08:55:05Z
dc.date.issued 2009-09-04
dc.identifier.citation Shen , Q , Ellis , I O , Garibaldi , J M & Rasmani , K A 2009 , ' Linguistic Rulesets Extracted from a Quantifier-based Fuzzy Classification System ' Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09) , pp. 1204-1209 . en
dc.identifier.other PURE: 129220
dc.identifier.other dspace: 2160/2909
dc.identifier.uri http://hdl.handle.net/2160/2909
dc.description K.A. Rasmani, J.M. Garibaldi, Q. Shen and I.O. Ellis. Linguistic Rulesets Extracted from a Quantifier-based Fuzzy Classification System. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 1204-1209, 2009. en
dc.description.abstract The use of linguistic rulesets is considered one of the greatest advantages that fuzzy classification systems can offer compared to non-fuzzy classification systems. This paper proposes the use of fuzzy thresholds and fuzzy quantifiers for generating linguistic rulesets from a data-driven fuzzy subsethood-based classification system. The proposed technique offers not only simplicity in the design and comprehensibility of the generated rulesets but also practicality in the implementation. Additionally, the use of fuzzy quantifiers makes it easier for the user to understand the classification process and how such classifications were reached. The effectiveness of the proposed method is demonstrated using a medical dataset which provides evidence that rules generated by the proposed system are consistent with the expert-rules created by clinicians. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09) en
dc.title Linguistic Rulesets Extracted from a Quantifier-based Fuzzy Classification System en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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