An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
This work addresses a specific problem for researchers and practitioners in machine learning who need to choose appropriate active learning techniques for outlier detection, but it is incremental as it focuses on benchmarking and comparison rather than introducing new methods.
The paper tackles the difficulty of selecting active learning methods for outlier detection with one-class classifiers by providing a comprehensive comparison, including categorization, evaluation proposals, and extensive experiments across various scenarios, resulting in practical guidelines for method selection.
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.