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Validating one-class active learning with user studies – A prototype and open challenges

Trittenbach, Holger 1; Englhardt, Adrian 1; Böhm, Klemens 1
1 Karlsruher Institut für Technologie (KIT)


Active learning with one-class classifiers involves users in the detection of outliers. The evaluation of one-class active learning typically relies on user feedback that is simulated, based on benchmark data. This is because validations with real users are elaborate. They require the de-sign and implementation of an interactive learning system. But without such a validation, it is unclear whether the value proposition of active learning does materialize when it comes to an actual detection of out-liers. User studies are necessary to find out when users can indeed provide feedback. In this article, we describe important characteristics and pre-requisites of one-class active learning for outlier detection, and how they influence the design of interactive systems. We propose a reference architecture of a one-class active learning system. We then describe design alternatives regarding such a system and discuss conceptual and technical challenges. We conclude with a roadmap towards validating one-class active learning with user studies.

Verlagsausgabe §
DOI: 10.5445/IR/1000099157
Veröffentlicht am 26.10.2019
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000099157
Erschienen in IAL 2019 Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany, September 16th, 2019. Ed.: D. Kottke
Veranstaltung Workshop on Interactive Adaptive Learning (IAL 2019), Würzburg, Deutschland, 16.09.2019
Seiten 17-31
Serie CEUR Workshop Proceedings ; 2444
Schlagwörter Active learning; one-class classification; outlier detection; user study
Nachgewiesen in Scopus
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