Non-deterministic outlier detection method based on the variable precision rough set model

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dc.contributorGrupoM. Redes y Middlewarees_ES
dc.contributor.authorFernández Oliva, Alberto-
dc.contributor.authorMaciá Pérez, Francisco-
dc.contributor.authorBerna-Martinez, Jose Vicente-
dc.contributor.authorAbreu Ortega, Miguel-
dc.contributor.otherUniversidad de Alicante. Departamento de Tecnología Informática y Computaciónes_ES
dc.identifier.citationInternational Journal of Computer Systems Science & Engineering. 2019, 3: 131-144es_ES
dc.description.abstractThis study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a comparison of the results with the RSBM-based method demonstrate the effectiveness of the method as well as the algorithm’s efficiency in diverse contexts, which also involve large amounts of data.es_ES
dc.description.sponsorshipThis study was funded by grant TIN2016-78103-C2-2-R and University of Alicante GRE14-02.es_ES
dc.publisherCRL Publishinges_ES
dc.rights© 2019 CRL Publishing Ltdes_ES
dc.subjectRough Sets (RS)es_ES
dc.subjectRS Basic Model (RSBM)es_ES
dc.subjectVariable Precision Rough Set Model (VPRSM)es_ES
dc.subjectData setes_ES
dc.subjectData Mininges_ES
dc.subject.otherArquitectura y Tecnología de Computadoreses_ES
dc.titleNon-deterministic outlier detection method based on the variable precision rough set modeles_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-78103-C2-2-R-
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