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

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2017

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Gesellschaft für Informatik, Bonn

Zusammenfassung

Many 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.

Beschreibung

Singhof, Michael; Klassen, Gerhard; Braun, Daniel; Conrad, Stefan (2017): Detection and Implicit Classification of Outliers via Different Feature Sets in Polygonal Chains. Datenbanksysteme für Business, Technologie und Web (BTW 2017). Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-659-6. pp. 237-246. Data Analytics. Stuttgart. 6.-10. März 2017

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