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dc.contributorOllé Torner, Mercè
dc.contributorCheney, James
dc.contributor.authorGombau Pascual, Xavier
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.coverage.spatialeast=-3.1892430782318115; north=55.94451979710907; name=7 George Square, Edinburgh EH8 9JZ, Regne Unit
dc.date.accessioned2020-07-30T17:07:51Z
dc.date.issued2020-07
dc.identifier.urihttp://hdl.handle.net/2117/328112
dc.description.abstractDetecting objects that deviate significantly from the rest of a dataset is a complex process which requires advanced techniques. A great variety of algorithms to detect anomalies have been presented over the last years, but none has been proved to be the best. We present a proxy technique for predicting the outlier detection performance of compression-based algorithms using the minimum description length (MDL) principle given a particular dataset. We analyse the correlation between how well an algorithm can compress the data and its performance in anomaly detection (AD). The results show a clear relationship between the total compressed size of a dataset and the outlier detection performance for an MDL-based algorithm. This fact allows us to use the size as a proxy for selecting the most effective AD algorithm for a specific application.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística
dc.subject.lcshAlgorithms
dc.subject.otherAnomaly detection
dc.subject.otherMinimum description length
dc.subject.otherProvenance
dc.subject.otherClustering
dc.titleAnomaly detection model selection using minimum description length
dc.typeMaster thesis
dc.subject.lemacAlgorismes
dc.subject.amsClassificació AMS::68 Computer science::68W Algorithms
dc.identifier.slugFME-1984
dc.rights.accessRestricted access - confidentiality agreement
dc.date.lift10000-01-01
dc.date.updated2020-07-17T09:24:47Z
dc.audience.educationlevelMàster
dc.audience.mediatorUniversitat Politècnica de Catalunya. Facultat de Matemàtiques i Estadística
dc.audience.degreeMÀSTER UNIVERSITARI EN MATEMÀTICA AVANÇADA I ENGINYERIA MATEMÀTICA (Pla 2010)
dc.contributor.covenanteeUniversity of Edinburgh. School of Informatics
dc.description.mobilityOutgoing


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