no code implementations • 29 Sep 2019 • Emille E. O. Ishida, Matwey V. Kornilov, Konstantin L. Malanchev, Maria V. Pruzhinskaya, Alina A. Volnova, Vladimir S. Korolev, Florian Mondon, Sreevarsha Sreejith, Anastasia Malancheva, Shubhomoy Das
We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets.
In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors.
First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.
Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.
The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.