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Detection and Implicit Classification of Outliers via Different Feature Sets in Polygonal Chains

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dc.contributor.authorSinghof, Michael
dc.contributor.authorKlassen, Gerhard
dc.contributor.authorBraun, Daniel
dc.contributor.authorConrad, Stefan
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-20T20:24:28Z
dc.date.available2017-06-20T20:24:28Z
dc.date.issued2017
dc.description.abstractMany outlier detection tasks involve a classification of outliers of di erent types. Most standard procedures solve this problem in two steps: First, an outlier detection algorithm is carried out, which is normally trained on outlier free data, only, since the samples of outliers are limited. Second, the outliers detected in that step, are classified with a conventional classification algorithm, that needs samples for all classes. However, often the quality of the classification is lowered due to the small number of available samples. Therefore, in this work, we introduce an outlier detection and classification algorithm, that does not depend on training data for the classification process. Instead, we assume, that di erent kinds of outliers are inferred by di erent processes and as such should be detected by different outlier detection approaches. This work focuses on the example of outliers in mountain silhouettes.en
dc.identifier.isbn978-3-88579-659-6
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-265
dc.subjectAnomaly & Outlier Detection
dc.subjectClassification
dc.subjectImage Segmentation
dc.titleDetection and Implicit Classification of Outliers via Different Feature Sets in Polygonal Chainsen
dc.typeText/Conference Paper
gi.citation.endPage246
gi.citation.startPage237
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleData Analytics

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