An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
Abstract
Active learning methods increase classification quality by means of user feedback. An important subcategory is active learning for outlier detection with one-class classifiers. 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. Based on our results, we formulate guidelines on how to select active learning methods for outlier detection with one-class classifiers.
Cite
@article{arxiv.1808.04759,
title = {An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers},
author = {Holger Trittenbach and Adrian Englhardt and Klemens Böhm},
journal= {arXiv preprint arXiv:1808.04759},
year = {2019}
}
Comments
Change history: update to more specific title; restructure of experimental section: added additional data sets and heuristic to select kernel parameter; add guidelines and decision rules (Section 4.4). Further minor changes: additional references; discussion of split strategies now is in Section 3.4; fixed typos