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dc.contributor.authorZhou, Shang-Ming
dc.contributor.authorGan, JQ
dc.date.accessioned2023-02-15T13:18:44Z
dc.date.available2023-02-15T13:18:44Z
dc.date.issued2008-12
dc.identifier.issn0165-0114
dc.identifier.issn1872-6801
dc.identifier.urihttp://hdl.handle.net/10026.1/20375
dc.description.abstract

This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low-level interpretability and high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested. Crown Copyright © 2008.

dc.format.extent3091-3131
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectData-driven fuzzy systems
dc.subjectInterpretable
dc.subjectFuzzy models
dc.subjectInterpretability
dc.subjectTransparency
dc.subjectCriteria
dc.subjectParsimony
dc.subjectDistinguishability
dc.subjectLow-level interpretability
dc.subjectHigh-level interpretability
dc.titleLow-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling
dc.typejournal-article
dc.typeReview
dc.typeJournal
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000260713000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue23
plymouth.volume159
plymouth.publication-statusPublished
plymouth.journalFuzzy Sets and Systems
dc.identifier.doi10.1016/j.fss.2008.05.016
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
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dc.identifier.eissn1872-6801
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.fss.2008.05.016
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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