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User-Centric Active Learning for Outlier Detection

Trittenbach, Holger

Abstract (englisch):
Outlier detection searches for unusual, rare observations in large, often high-dimensional data sets.
One of the fundamental challenges of outlier detection is that ``unusual'' typically depends on the perception of a user, the recipient of the detection result.
This makes finding a formal definition of ``unusual'' that matches with user expectations difficult.
One way to deal with this issue is active learning, i.e., methods that ask users to provide auxiliary information, such as class label annotations, to return algorithmic results that are more in line with the user input.
Active learning is well-suited for outlier detection, and many respective methods have been proposed over the last years.
However, existing methods build upon strong assumptions.
One example is the assumption that users can always provide accurate feedback, regardless of how algorithmic results are presented to them -- an assumption which is unlikely to hold when data is high-dimensional.
It is an open question to which extent existing assumptions are in the way of realizing active learning in practice.

In this thesis, we study this question from different perspectives with a differentiated, user-centric view on active learning.
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Volltext §
DOI: 10.5445/IR/1000117443
Veröffentlicht am 02.03.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Publikationsdatum 02.03.2020
Sprache Englisch
Identifikator KITopen-ID: 1000117443
Verlag Karlsruhe
Umfang xi, 129 S.
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdatum 05.02.2020
Referent/Betreuer Prof. K. Böhm
Schlagwörter Active Learning, Outlier Detection, One-Class Classification
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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