Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis

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dc.contributor.authorZhang, Xiao
dc.contributor.authorOnieva, Enrique
dc.contributor.authorPerallos, Asier
dc.contributor.authorOsaba, Eneko
dc.contributor.institutionQuantum
dc.date.accessioned2024-07-24T12:03:48Z
dc.date.available2024-07-24T12:03:48Z
dc.date.issued2020
dc.descriptionPublisher Copyright: © 2020 Inderscience Enterprises Ltd.
dc.description.abstractAccurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.en
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61902437, the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities under Grant CZT19010, and the Research Start-up Funds of South-Central University for Nationalities under Grant YZZ18006.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationZhang , X , Onieva , E , Perallos , A & Osaba , E 2020 , ' Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis ' , International Journal of Bio-Inspired Computation , vol. 15 , no. 3 , pp. 194-205 . https://doi.org/10.1504/IJBIC.2020.107490
dc.identifier.doi10.1504/IJBIC.2020.107490
dc.identifier.issn1758-0366
dc.identifier.urihttps://hdl.handle.net/11556/3389
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85086032519&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInternational Journal of Bio-Inspired Computation
dc.relation.projectIDResearch Start-up Funds of South-Central University for Nationalities
dc.relation.projectIDNational Natural Science Foundation of China, NSFC, 61902437
dc.relation.projectIDSouth-Central University of Nationalities, SCUN, YZZ18006-CZT19010
dc.relation.projectIDFundamental Research Funds for the Central Universities
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsBreast cancer diagnosis
dc.subject.keywordsClassification system
dc.subject.keywordsFuzzy logic
dc.subject.keywordsGenetic algorithm
dc.subject.keywordsVariable selection
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleGenetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosisen
dc.typejournal article
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