Linguistic Rulesets Extracted from a Quantifier-based Fuzzy Classification System

Khairul Rasmani, Jonathan M. Garibaldi, Qiang Shen, Ian O. Ellis

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

8 Citations (SciVal)
231 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09)
PublisherIEEE Press
Number of pages6
ISBN (Electronic)978-1-4244-3597-5
ISBN (Print)978-1-4244-3596-8
Publication statusPublished - Aug 2009
EventFuzzy Systems - Jeju Island, Korea (Republic of)
Duration: 20 Aug 200924 Aug 2009
Conference number: 18


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2009
Country/TerritoryKorea (Republic of)
CityJeju Island
Period20 Aug 200924 Aug 2009


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