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dc.contributor.authorZhou, S-Men
dc.contributor.authorLyons, RAen
dc.contributor.authorBrophy, Sen
dc.contributor.authorGravenor, MBen
dc.date.accessioned2021-02-04T22:19:24Z
dc.date.available2021-02-04T22:19:24Z
dc.date.issued2012en
dc.identifier.urihttp://hdl.handle.net/10026.1/16850
dc.description.abstract

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.

en
dc.format.extente51468 - ?en
dc.languageengen
dc.language.isoengen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectAlgorithmsen
dc.subjectChilden
dc.subjectCluster Analysisen
dc.subjectComputational Biologyen
dc.subjectComputer Simulationen
dc.subjectComputersen
dc.subjectData Miningen
dc.subjectDatabases, Factualen
dc.subjectDecision Makingen
dc.subjectEpidemiologyen
dc.subjectFuzzy Logicen
dc.subjectHealth Surveysen
dc.subjectHumansen
dc.subjectModels, Statisticalen
dc.subjectModels, Theoreticalen
dc.subjectNeural Networks, Computeren
dc.subjectSoftwareen
dc.titleConstructing compact Takagi-Sugeno rule systems: identification of complex interactions in epidemiological data.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/23272108en
plymouth.issue12en
plymouth.volume7en
plymouth.publication-statusPublisheden
plymouth.journalPLoS Oneen
dc.identifier.doi10.1371/journal.pone.0051468en
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/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeUnited Statesen
dcterms.dateAccepted2012-11-07en
dc.identifier.eissn1932-6203en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1371/journal.pone.0051468en
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/en
rioxxterms.licenseref.startdate2012en
rioxxterms.typeJournal Article/Reviewen


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