Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation

Tianhua Chen, Qiang Shen, Pan Su, Changjing Shang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.
Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781467374286
Publication statusPublished - 30 Nov 2015
Externally publishedYes


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