KIT | KIT-Bibliothek | Impressum | Datenschutz

An Overview and a Benchmark of Active Learning for One-Class Classification

Trittenbach, Holger; Englhardt, Adrian; Böhm, Klemens


Active learning stands for methods which increase classification quality by means of user feedback. An important subcategory is active learning for one-class classifiers, i.e., for imbalanced class distributions. While various methods in this category exist, selecting one for a given application scenario is difficult. This is because existing methods rely on different assumptions, have different objectives, and often are tailored to a specific use case. All this calls for a comprehensive comparison, the topic of this article. This article starts with a categorization of the various methods. We then propose ways to evaluate active learning results. Next, we run extensive experiments to compare existing methods, for a broad variety of scenarios. One result is that the practicality and the performance of an active learning method strongly depend on its category and on the assumptions behind it. Another observation is that there only is a small subset of our experiments where existing approaches outperform random baselines. Finally, we show that a well-laid-out categorization and a rigorous specification of assumptions can facilitate the selection of a good method for one-class classification.

Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator KITopen-ID: 1000085330
Erschienen in arXiv preprint 1808.04759
Projektinformation GRK 2153/1 (DFG, DFG KOORD, GRK 2153/1)
Schlagwörter active learning, one-class classification, support-vector data description, outlier detection, user feedback
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page