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A hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development

dc.contributor.authorKalibatienė, Diana
dc.contributor.authorMiliauskaitė, Jolanta
dc.date.accessioned2023-09-18T20:43:10Z
dc.date.available2023-09-18T20:43:10Z
dc.date.issued2021
dc.identifier.issn0868-4952
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/151991
dc.description.abstractThe data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.eng
dc.formatPDF
dc.format.extentp. 85-118
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbySocial Sciences Citation Index (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyGenamics Journal Seek
dc.relation.isreferencedbyZentralblatt MATH (zbMATH)
dc.relation.isreferencedbyMathematical Reviews/MathSciNet
dc.relation.isreferencedbyVINITI
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://doi.org/10.15388/21-INFOR444
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:89071235/datastreams/MAIN/content
dc.titleA hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsOpen access article under the CC BY license.
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references142
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionVilniaus universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionB04 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsIK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enmembership function
dc.subject.enfuzzy rule
dc.subject.enfuzzy inference system
dc.subject.enissue
dc.subject.enlimitation
dc.subject.encomplexity
dc.subject.ensystematic literature review
dc.subject.ensystematic mapping
dcterms.sourcetitleInformatica
dc.description.issueiss. 1
dc.description.volumevol. 32
dc.publisher.nameVilnius University
dc.publisher.cityVilnius
dc.identifier.doi000640109800005
dc.identifier.doi10.15388/21-INFOR444
dc.identifier.elaba89071235


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