An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers

TL;DR

This paper provides a comprehensive overview and benchmarking of active learning methods for outlier detection using one-class classifiers, offering guidance for selecting appropriate methods across diverse scenarios.

Contribution

It categorizes existing active learning methods for outlier detection, proposes evaluation strategies, and conducts extensive experiments to compare their performance, resulting in practical guidelines.

Findings

Different methods perform variably across scenarios

Evaluation strategies influence method selection

Guidelines help choose suitable active learning approaches

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.