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Calibration and Evaluation of Outlier Detection with Generated Data

Steinbuß, Georg

Abstract (englisch):

Outlier detection is an essential part of data science --- an area with increasing relevance in a plethora of domains. While there already exist numerous approaches for the detection of outliers, some significant challenges remain relevant. Two prominent such challenges are that outliers are rare and not precisely defined. They both have serious consequences, especially on the calibration and evaluation of detection methods. This thesis is concerned with a possible way of dealing with these challenges: the generation of outliers. It discusses existing techniques for generating outliers but specifically also their use in tackling the mentioned challenges. In the literature, the topic of outlier generation seems to have only little general structure so far --- despite that many techniques were already proposed. Thus, the first contribution of this thesis is a unified and crisp description of the state-of-the-art in outlier generation and their usages. Given the variety of characteristics of the generated outliers and the variety of methods designed for the detection of real outliers, it becomes apparent that a comparison of detection performance should be more distinctive than state-of-the-art comparisons are. ... mehr

Volltext §
DOI: 10.5445/IR/1000120534
Veröffentlicht am 03.07.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Publikationsdatum 03.07.2020
Sprache Englisch
Identifikator KITopen-ID: 1000120534
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xx, 117 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdatum 12.05.2020
Referent/Betreuer Böhm, K.
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